international journal of research in pharmaceutical sciences

International Journal of Research in Pharmaceutical Sciences

Discontinued in Scopus as of 2020

Subject Area and Category

• Pharmacology, Toxicology and Pharmaceutics (miscellaneous)

J. K. Welfare and Pharmascope Foundation

Publication type

The set of journals have been ranked according to their SJR and divided into four equal groups, four quartiles. Q1 (green) comprises the quarter of the journals with the highest values, Q2 (yellow) the second highest values, Q3 (orange) the third highest values and Q4 (red) the lowest values.

The SJR is a size-independent prestige indicator that ranks journals by their 'average prestige per article'. It is based on the idea that 'all citations are not created equal'. SJR is a measure of scientific influence of journals that accounts for both the number of citations received by a journal and the importance or prestige of the journals where such citations come from It measures the scientific influence of the average article in a journal, it expresses how central to the global scientific discussion an average article of the journal is.

Evolution of the number of published documents. All types of documents are considered, including citable and non citable documents.

This indicator counts the number of citations received by documents from a journal and divides them by the total number of documents published in that journal. The chart shows the evolution of the average number of times documents published in a journal in the past two, three and four years have been cited in the current year. The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric.

Evolution of the total number of citations and journal's self-citations received by a journal's published documents during the three previous years. Journal Self-citation is defined as the number of citation from a journal citing article to articles published by the same journal.

Evolution of the number of total citation per document and external citation per document (i.e. journal self-citations removed) received by a journal's published documents during the three previous years. External citations are calculated by subtracting the number of self-citations from the total number of citations received by the journal’s documents.

International Collaboration accounts for the articles that have been produced by researchers from several countries. The chart shows the ratio of a journal's documents signed by researchers from more than one country; that is including more than one country address.

Not every article in a journal is considered primary research and therefore "citable", this chart shows the ratio of a journal's articles including substantial research (research articles, conference papers and reviews) in three year windows vs. those documents other than research articles, reviews and conference papers.

Ratio of a journal's items, grouped in three years windows, that have been cited at least once vs. those not cited during the following year.

Evolution of the percentage of female authors.

Evolution of the number of documents cited by public policy documents according to Overton database.

Evoution of the number of documents related to Sustainable Development Goals defined by United Nations. Available from 2018 onwards.

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Identifiers

Linking ISSN (ISSN-L): 0975-7538

URL http://www.ijrps.pharmascope.org

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Resource information

Title proper: International journal of research in pharmaceutical sciences.

Country: India

Medium: Online

Record information

Last modification date: 07/02/2021

Type of record: Confirmed

ISSN Center responsible of the record: ISSN National Centre for India For all potential issues concerning the description of the publication identified by this bibliographic record (missing or wrong data etc.), please contact the ISSN National Centre mentioned above by clicking on the link.

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Illuminating the Molecular Operations Through Study of Protein Interactions

Formulation and Evaluation of Herbal Face Cream with Green Tea Extract

Role of Artificial Intelligence in Drug Development

Cancer and Neurodegenerative Disorders: Pathogenic Convergence Through MicroRNA Regulation

Elucidating Genomic Instability and Its Outcomes as Cancer Origin

Antibacterial Activity of AegleMarmelos Against Fruit Extracts

In Vitro Antiviral Activity of AegleMarmelos Against Influenza A (H1N1) Pdm09

Analytical Method Development and Validation for Simultaneous Estimation of Moxifloxacin Hydrochloride and Ketorolac Tromethamine by Using RP-HPLC

Novel Approaches to Enhance Oral Bioavailability of Poorly Soluble Drugs

Simultaneous Determination of Levocetrizine and Phenylpropanolamine Hydrocholride by RP-HPLC

Bromelain as an Anti-inflammatory and Anti-cancer Compound

Anthelmintic Activity of Leaves Extracts of Olea Europaea on Pheretima Posthuma

Preclinical Toxicological Evaluation of Aloe Vera Health Drinks in Wistar Rats

Formulation and Invitro Evaluation of Oral Extended Release Microspheres of Aceclofenac Using Various Natural Polymers

Surgical Glue- a Promising Technology for Wound Healing

Osmotic Drug Delivery System of Valsartan

Phytochemical, Physicochemical, TLC, Minerals Analysis and In-vitro Antioxidant Activity of Ethanolic Extract of Leaves of Heldigardia Populifolia

Development and Validation of New Analytical Method for the Simultaneous Estimation of Amitriptyline and Perphenazine in Bulk and Pharmaceutical Dosage Form by RP-HPLC

Role of Cationic Lipids for the Formulation of Lipoplexes

Enhancement of Dissolution Rate of Olmesartan Medoxomil Using Urea as Carrier by Different Solid Dispersion Techniques

An Overview on Comparative Study of Registration Requirements for Generics in US, Canada and Europe

Formulation, Optimization and Characterization of Betaxolol Hydrochloride Proniosomes Using 3-2 Factorial Design

Carbon Nanotubes: A Promising Novel Drug Delivery System

A Review on Anitibiotic Resistance

Formulate and Evaluate Once Daily Sustained Release Tablet of Highly Soluble Drug of Metformin HCL

Analytical Method Development and Validation for the Estimation of Mirabegron in Pure and Its Solid Dosage Form by UV Spectrophotometric Method

Formulation, Design, Optimization and Evaluation of Cimetidine Floating Tablets by Using 32 Factorial Design

Assessment of In-Vitro Cytotoxicity and In-Vivo Antitumor Activity of Syzygium Aqueum

Formulation and Evaluation of Fast Disintegrating Tablets of Metoprolol Succinate Using Various Superdisintegrants

A Comprehensive Overview on Nanofiber TECHNOLOGY and Their Advancements in the Field of Pharmaceuticals

Research in Pharmaceutical Sciences

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Journal logo, current issue.

May-Jun 2024 - Volume 19 - Issue 3

• Editor-in-Chief: Prof. Jaber Emami
• ISSN: 1735-5362
• Online ISSN: 1735-9414
• Frequency: Quarterly
• Impact Factor: 2.1

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Current Issue Highlights

• Original Article

Pharmacological evaluation of anti-inflammatory, antipyretic, analgesic, and antioxidant activities of Castanopsis costata leaf fractions (water, ethyl acetate, and n -hexane fractions): the potential medicinal plants from North Sumatra, Indonesia

Research in Pharmaceutical Sciences. 19(3):251-266, May-Jun 2024.

• Abstract Abstract
• Castanopsis costata</em> leaf fractions (water, ethyl acetate, and <em xmlns:mrws=\"http://webservices.ovid.com/mrws/1.0\">n</em>-hexane fractions): the potential medicinal plants from North Sumatra, Indonesia', '07012024', 'Maulana Yusuf Alkandahri, Asman Sadino, Barolym Tri Pamungkas, et al', '19', '3', 'Copyright: \u00A9 2024 Research in Pharmaceutical Sciences', '01339178-202419030-00001', '', true, '')" href="javascript:"> Permissions

Go to Full Text of this Article

Spiroconjugated 1,2,3-triazolo[5,1- b ]1,3,4-thiadiazine stimulates functional activity of fibroblasts under skin injury regeneration

Research in Pharmaceutical Sciences. 19(3):267-275, May-Jun 2024.

• b</em>]1,3,4-thiadiazine stimulates functional activity of fibroblasts under skin injury regeneration', '07012024', 'Irina M Petrova, Sofya Iu Chebanova, Sergey L Khatsko, et al', '19', '3', 'Copyright: \u00A9 2024 Research in Pharmaceutical Sciences', '01339178-202419030-00002', '', true, '')" href="javascript:"> Permissions

Effect of Tamarindus indica L. fruit pulp and seed extracts on experimental ulcerative colitis in rats

Research in Pharmaceutical Sciences. 19(3):276-286, May-Jun 2024.

• Tamarindus indica</em> L. fruit pulp and seed extracts on experimental ulcerative colitis in rats', '07012024', 'Mohsen Minaiyan, Sepehr Abolhasani, Setareh Sima, et al', '19', '3', 'Copyright: \u00A9 2024 Research in Pharmaceutical Sciences', '01339178-202419030-00003', '', true, '')" href="javascript:"> Permissions

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• ISSN0975-4725

International Journal of Pharmaceutical Sciences

An open access peer-reviewed journal aiming to communicate high quality original research work that contribute scientific knowledge related to the field of Pharmaceutical Sciences.

(ISO 9001:2015 Certified International Journal) ISSN: 0975-4725; CODEN(USA): IJPS00

Getting the work published is as effortless as you could imagine. IJPS makes it easy for all those who want to get their work published.

Authors can access their accounts to check the status of their articles. Important mail notifications are sent to authors on a regular basis.

The Journal always excited to achieve a genuine journal Impact Factor & approved indexing. we have already achieved a list of a milestone.

Publishing Quality Work

Important notifications, impact factor® is 4.7.

Know About Us

International Journal of Pharmaceutical Sciences is online open access journal. Publishing article from many country in the field of Pharmaceutical Sciences.

• Peer-Reviewed Journal.
• Strict Policy against Plagiarism.

Guidance to Enhance the Quality of Research.

LATEST ARTICLES

Read our recently published articles from current issue

Controlled Release Medication Delivery Systems: Us...

Systems for delivering drugs with controlled release have many benefits over traditional dose f...

Benzimidazole: A Versatile Pharmacophore For Diver...

Benzimidazole, a bicyclic compound formed by the fusion of benzene and imidazole rings, is reco...

Exploring The Multifaceted Mechanisms Of Amphetami...

Amphetamine (AMPH) and its derivatives, exhibiting diverse structures and psychoactive effects,...

Formulation and Evaluation Herbal Face Pack Powder...

The aim of this work is to formulate and evaluate an herbal face Pack for glowing skin by using...

A Study On Antimicrobial Activity Of Lantana Camar...

Plants have a great number of chemicals that can be exploited as valuable sources of natural an...

Formulation And Evaluation Of Polyherbal Hair Oil...

Objective: The goal of this study is to formulate and evaluate polyherbal hair oil for hair f...

Oncopathology And Cancer Immunotherapy...

Immunotherapy emerged as a promising strategy cancer treatment, revolutionizing the field by ha...

Formulation and Evaluation of Herbal Shampoo ...

The main object of this present study is to study an herbal shampoo and determine physiochemica...

Comparative Study Of Different Polymer Based Emulg...

Emulgel, which contain a dual release control mechanism that includes both a gel and an emulsio...

OUR STATISTICS

Issue Released

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The Power of Process Analytical Technology (PAT) in Pharmaceutical Manufacturing

Technological innovations throughout the past several decades have transformed the pharmaceutical manufacturing process from traditional batch production to autonomous and continuous manufacturing.

Not just a smokers' disease: Breaking the lung cancer stigma

Lung cancer is the second most common form of cancer worldwide, leading to around 20% of cancer-related deaths. We know that smoking can cause lung cancer, but it is less known that 15–20%

Frequently Asked Questions (FAQ's)

About International Journal of Pharmaceutical Sciences

Anybody can submit their articles by online system. But in case of problem by online submission system please inform and submit manuscript as an e-mail attachment to [email protected]

It takes 3-4 days for peer review process of any paper.

Journal strictly follows UGC care guidelines.

Visit check the status page on website. Check Paper Status

Authors of research articles keep copyright of their work. Articles are licensed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) (https://creativecommons.org/licenses/by/4.0/legalcode), which permits the unrestricted, non-commercial use, distribution and reproduction in any medium, provided that the work is properly cited.

The journal is published online every month. This means that articles are posted online as they are accepted and processed within the same month. Refer the link for more details: https://www.ijpsjournal.com/archive

The impact factor of IJPS is 4.7 and it is periodically updated and can be found on the About Us page. It reflects the average number of citations received per paper published in the journal. Refer the link for more details: https://www.ijpsjournal.com/about-us

Posting of articles which are submitted to IJPS on websites before acceptance and publication may be considered prior publication. Authors should refrain from posting their articles until they have been published. Authors may give the article title and abstract with a link to the journal home page. It is against copyright laws to post the entire article on another website.

Copyright © 2024 IJPS. All rights reserved

Author Guidelines

Instructions to Authors

MANUSCRIPT SUBMISSION PROCEDURES AND INSTRUCTIONS

Communication with Editor

Scope and Coverage of Manuscripts

The IJPSN (UGC approved journal) invites manuscripts dealing with broad areas and all aspects of pharmaceutical sciences, furthering research into the nanotechnology systems. All aspects of pharmaceutics, biopharmaceutics, industrial pharmacy, clinical pharmacy, pharmacology and therapeutics, toxicology, pharmaceutical chemistry, phytochemical investigations, medicinal and analytical chemistry, pharmacy administration, regulatory affairs, drug approval process, and pharmacoeconomics, pharma biotechnology, recombinant drugs, and all aspects of nanotechnology-based drug formulation design and delivery systems are considered for publication in the Journal.  Manuscripts that were previously published in part or full or papers that are under consideration elsewhere are NOT acceptable.  

The Journal considers the following three broad categories of manuscripts for publication:

• Research Papers : These are manuscripts predominantly devoted to reporting ORIGINAL research investigations in the pharmaceutical and biomedical sciences, including research in one of the core pharmaceutical sciences (pharmaceutics, biopharmaceutics, industrial pharmacy, clinical pharmacy, pharmacology and therapeutics, toxicology, pharmaceutical chemistry, phytochemical investigations, medicinal and analytical chemistry, pharmacy administration and pharmacoeconomics, pharma biotechnology, novel drug delivery, drug discovery; clinical trials of therapeutic agents, diagnostic techniques, behavioral, epidemiological or educational aspects of pharmacy). Papers from clinical or pharmacy practice or any related pharmaceutical topics will be considered for publication. Basic science research with pharmaceutical applications is also considered for publication. The manuscript should adhere to the following format and page limitations (see below). 
• Review Articles : Review articles should contain the current state of knowledge or recent advances in the field, integrating basic background principles and practice, or summarizing with a critical analysis of the field or consensus view of controversial topics in pharmaceutical field. Review articles should provide comprehensive and updated information of the topic that and be presented in a quite useful format for researchers, students, industrial scientists, and others professionals. Broad topic areas including new drug monographs, nanotechnology or novel drug delivery systems are particularly encouraged. There is no specific structure or format for Review Articles, but it should carry major headings, subheadings and headings of topic discussions with suitable reference citations. Manuscripts must not be more than 50 single-spaced pages including references (excluding figures and tables). 
• Letter to Editor - Template  

Submission of Manuscripts

Online Submission via Website OJMS.

Submit your manuscript as per the instructions provided. Attach your entire manuscript as single Word file (including tables and figures). High-resolution figures should be submitted with each manuscript.  After submitting your manuscript, you will receive an acknowledgement. 

A scanned copy of the Copyright Form signed by the correspondent author and ALL co-authors should be submitted along with the manuscript. A PDF copy of the Copyright Form is available online (see “Instructions for Authors). Submission of a manuscript represent an assurance that the paper has received the author's institutional permissions, if any, has not been copyrighted, published, or accepted for publication elsewhere, is not currently being considered for publication in other journals, and will not be submitted to any other journal while under review and consideration by the International Journal of Pharmaceutical Sciences and Nanotechnology .  

Organization of the Manuscript  

Manuscripts must be in Word file using Times New Roman 12 font only, and single-spaced throughout, including references, tables, and figure legends. Manuscripts should contain the following sections in the order listed. 

• Title page. This page should contain the complete title of the article, the names of all authors, and their institutional address and the address of the correspondence author.  The title page should contain the following: 

(a) Article title – it should be brief and convey the overall finding or nature of the manuscript or research study.  Indicate on the top “Research Paper” or “Review Article” as appropriate. 

(b)   Authors . Names of all authors and their institutional address.  Departmental or institutional affiliation should be indicated by author initials only.

(c) Running title : A running title, which conveys the sense of the full title (not to exceed 10 words) may be used. 

(d) Manuscript statistics. The number of text pages, number of tables, figures, and references, and the number of words in the Abstract, Introduction, and Discussion (provide each item on a separate line). 

(e)   Corresponding author . The name, address, telephone and e-mail address of the corresponding author.

• Abstract. The abstract should concisely present the background of the topic, purpose of the study, research rationale, general methods, results, and conclusions. The abstract should not exceed 300 words . The abstract must be a single paragraph. Key words : Provide 6 to 10 key words of the research paper. Abbreviations : Provide a list of abbreviations used in the manuscript. 
• Introduction. The Introduction section must contain a brief description of the relevant background that supports the rationale of the study. It must contain a clear description of the aims and objectives of the research paper or review article. The length of the Introduction section should not exceed 1000 words .
• Materials and Methods. The Methods or Experimental section should contain complete descriptions of all methods and procedures used in the study. They should be accompanied with suitable references, including the citation of the original source. The methods section should be organized under major headings. The name and address of commercial suppliers of chemicals, drugs, reagents, or drug samples and all equipment used must be listed in the Methods section. There is no word limitation for Methods section
• Results. The Results section should describe the experimental data and their statistical significance as appropriate.  Use paragraphs with headings stating the main findings. Results should be illustrated in figures or tables. Use units consistently throughout the manuscript. Generally, units are abbreviated without punctuation and with no distinction between singular and plural forms (e.g., 5 ml; 5 mg; 5 mg/ml; 5 mg/kg etc). Sufficient data should be presented to allow for judgment of the variability and reliability of the results. Statistical probability ( p ) in tables, figures, and figure legends should be expressed as * p <0.05, ** p <0.01, and *** p < 0.001. For second comparisons, one, two, or three daggers may be used. For multiple comparisons within a table, footnotes italicized in lower case, superscript letters are used and defined in the table legend. There is no word limitation for the Results section, but a succinct description would look better for reading. 
• Discussion. The Discussion section should describe the main conclusions drawn from the findings and their relevance to the field or advancing the field.  The main findings must be integrated with the current literature with a positive and negative confirmation or new findings with suitable literature references to support the new contentions or findings. The Discussion section should not exceed 2000 words.  
• Acknowledgments. The Acknowledgments section should be placed at the end of the text. Personal, technical or reagent help and assistance is noted here. Grants or financial support is acknowledged in this section. 

Reference formatting: Journal article:

Authors (Year). Title. Journal name, volume, page numbers Example:  Dharadhar S, Majumdar A, Dhoble S, Patravale V. Microneedles for transdermal drug delivery: a systematic review. Drug development and industrial pharmacy. 2019 Feb 1;45(2):188-201.

Book Chapter: Raja A, Mahendiratta S, Singh H, Shekhar N, Prakash A, Medhi B. Nanoparticles in Chronic Respiratory Diseases. InAdvanced Drug Delivery Strategies for Targeting Chronic Inflammatory Lung Diseases 2022 (pp. 143- 170). Springer, Singapore.

Book: Espnes GA. Salutogenesis: the Book’s editors discuss possible futures. The handbook of Salutogenesis. 2017 Jun 8:437. 

Note: Websites references are NOT acceptable.

• Tables. Each table should be single-spaced and formatted as Table on a separate page. Tables should be placed at the end of the manuscript after the References section. Tables are numbered consecutively. A brief descriptive title should be provided at the top of each table. 
• Legends for Figures. Each figure should be numbered consecutively and should be placed at the end of the manuscript along with legends. Legends must provide a clear explanation of each figure with brief title and other related information, including appropriate statistical markings (e.g., *p<0.01 vs. control). The type of statistical tests used may be indicated in the legend.   
• Figures. Figures should be prepared using Graphing software. Each figure must be prepared at minimum 500 DPI resolution in TIFF or JPG format. Figures should be ready for direct printing without further changes. Figure should fit either in a 3.5 inch SIGNLE column or 7-inch DOUBLE columns of a printed page. Label figure files with the figure number and author name. Photos, photomicrographs, and electron micrographs must be labeled with a calibration (scale bar) in micrometers, nanometers or Angstrom units. NOTE: Color figures will be published in the online version only. The print version is set for gray scale (B&W format) printing.  

List of Expert Reviewers

Each manuscript should be accompanied by a list of expert reviewers or researchers in the field. Provide at least three (3) suggested expert reviewers who are familiar with the research study and are willing to peer review the manuscript. In the cover letter, provide suggested reviewer’s name, position, institutional address and e-mail ID.  

Fee. Publication is FREE. There are no page charges in IJPSN.  

Manuscripts Processing. Each manuscript will be reviewed by multiple experts in the field. The reviewers are asked to evaluate the scientific merit of the study and provide suggestions for revision or improvements in the manuscript. The Chief Editor will take the final decision about the acceptance and suitability of the manuscript for publication. If the Chief Editor requests for revisions, the authors must submit revised manuscripts within the designated time (90 days), and must contain an itemized list of all changes made as per the reviewers' suggestions. 

Acceptance and Page Proofs. The Chief Editor will send notification about the acceptance of the manuscript or need for a revised version. All accepted manuscripts would be forwarded to the Publisher for setting up the page proofs as per the Journal style. Page proofs will be sent to the Correspondent Author by e-mail.  Page proofs should be corrected and returned to the Publisher within 5 days after receipt. Author are fully responsible for mistakes or errors in their accepted papers.

Submission Checklist

(1) Cover Letter. Submit cover letter to the Chief Editor on Institute Letterhead along with list of suggested reviewers. Cover letter is needed on institutional letterhead, signed by the corresponding author, supervisor and attested by the Principal (supervisor should attest that he/she has read the manuscript and approving it for submission to the journal). Principal should attest on the original work done at institute. 

(2) Copyright Form . Provide a scanned copy of Copyright Form signed by ALL authors. Copyright form is needed with original signatures of all authors. Fill the attached copyright form and send a color scanned copy (signed by all authors on same form).  

(3) Manuscript Word file. Provide Provide a single Word file with full text, tables and figures with legends. Organize the file in this order: Title page, Abstract, Abbreviations, Introduction, Results, Discussion, References, Tables and Figures with legends.  Insert all Figures in the Word file along with figure legends.  Figures should be prepared professionally and in JPG or TIFF format (minimum 500 dpi resolution). References . Arrange them alphabetically and check for proper citations in the text (e.g. Rao et al., 2006).

(4) Undertaking for Submission : Kindly submitted duly filled Undertaking and submit along with final submission.

Other Information

__Communication by Email . Chief Editor: [email protected]

__Publication is FREE. There is no page charge or any other fee for publishing papers in IJPSN.

__UGC-CARE Journal.   The IJPSN is a Print & Online-Access journal. All articles are accessible at the website ( www.ijpsnonline.com ).

__IJPSN Best Paper Award. The IJPSN Best Paper Award is given annually to one excellent paper published in IJPSN. The award consists of an award plaque and printed certificate. The Award winners will be notified directly by the Editorial team.

make submission

Article template

Author Resources

   Author Guidelines

  FAQ's-(Frequently Asked Question's)

  Review Process

    Copyright Form

   Cover Letter Sample

    Model Research Paper

    Model Review Paper

    Refrences Style Template

    Letter to Editor Template

    Undertaking for Submission

Meet Our Editorial Team

Editor–in–Chief

Dr. Bikash Medhi

Professor, Department of Pharmacology,

PGIMER (Postgraduate Institute of Medical Education & Research),

Chandigarh, 160012, India.

email id :[email protected]

Current Volume Issues

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Information

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• For Authors
• For Librarians

ISSN (Online): 0975-8232, ISSN (Print): 2320-5148

International Journal Of Pharmaceutical Sciences And Research

An International Journal published monthly An Official Publication of Society of Pharmaceutical Sciences and Research

BENZOTHIAZOLE – A MAGIC MOLECULE

Posted in Blog

CHEMICAL SYNTHESIS OF BILE ACIDS AND THEIR PHYSICO-CHEMICAL PROPERTIES

An overview on covid-19 outbreak: epidemic to pandemic, abstracting and indexing information, ijpsr updates.

• Volume 15, Issue 8, August 2024 of IJPSR is available online
• An International online and print Journal published monthly

Why publish with us?

• Worldwide dissemination through open access
• Immediate access of research of global audience
• Authoritative & constructive peer review process
• Prompt review
• Indexed with most international bibliographic databases
• Regular E- alert and sms alert

Volume 15 (2024) - Issue 8, August

Review articles, the role of granulocyte-colony stimulating factor for the management of chemotherapy-induced neutropenia in acute lymphoblastic leukemia.

Chemotherapy-induced neutropenia (CIN) is a common and sometimes fatal side effect of treatment for acute lymphoblastic leukemia (ALL), the most common malignancy in individuals over 40. Antibiotics are used in addition to supportive care as a current treatment. Granulocyte-colony stimulating factor (G-CSF) has been tested in clinical studies as a supplementary treatment to reduce febrile neutrop...

S. Hariharan *, V. S. Vimalan, Elayabhaarathe Thangaraj, K. Sudharsan and Jayalakahsmi Venugopal

Department of Hematology and Oncology, Kovai Medical Center and Hospital, Coimbatore, Tamil Nadu, India.

DOI: 10.13040/IJPSR.0975-8232.15(8).2191-97

• HTML Full Text

A REVIEW ARTICLE ON ANTIOXIDANT PROFILE OF BLUE TEA POLYPHENOLS IN THE TREATMENT OF VARIOUS DISEASES

Clitoria ternatea (Blue tea) is a traditional medicinal plant belonging to family Fabaceae having generous amount of anthocyanins which possess majorly antioxidant properties. It contains wide range of phytoconstituents such as triterpenoids, flavanol glycosides, tannins, alkaloids, amino acids, proteins, ternatins (poly acylated anthocyanins) and carbohydrates etc. According to the data obtained ...

Diksha *, Harshpreet Kaur and Jasneet Kaur

CT College of Pharmacy, Shahpur, Jalandhar, Punjab, India.

DOI: 10.13040/IJPSR.0975-8232.15(8).2198-09

BIOMARKER LANDSCAPE OF MYCOSIS, WITH SPECIAL EMPHASIS ON MUCORMYCOSIS

Systemic fungal infections, including opportunistic and endemic mycoses, pose a significant threat to immunocompromised individuals. Mucormycosis, caused by zygomycetes, is particularly devastating, characterized by tissue necrosis and diverse clinical manifestations. Risk factors such as immunodeficiency and underlying diseases contribute to its incidence. Through a comprehensive literature analy...

V. Poorna Harika and Richa Jain *

Department of Biotechnology and Microbiology, Centre for Scientific Research and Development, People's University, Bhopal, Madhya Pradesh, India.

DOI: 10.13040/IJPSR.0975-8232.15(8).2210-21

A NOVEL APPROACH TO TREAT NEUROLOGICAL DISORDER BY USING LIPID-BASED DRUG DELIVERY SYSTEM

One in every three people suffers from some kind of neurological disorder. Considering the huge burden of the disease, it is imperative to develop and treat such conditions. Because of time constraints and obstacles, treating neurological disorders remains challenging for medical professionals. The number of new pharmacologically active lipid compounds discovered using current drug development met...

Pooja Agarwal * and Vasudha Bakshi

School of Pharmacy, Anurag University, Venkatapur, Ghatkesar, Medchal District, Hyderabad, Telangana, India.

DOI: 10.13040/IJPSR.0975-8232.15(7).2222-29

NUTRIMILLET INSIGHTS: A REVIEW OF MILLET VARIETIES AND ADVANCED PROCESSING TECHNIQUES FOR ENHANCED NUTRITIONAL IMPACT

Millets are a group of small-seeded, grains that have gained renewed attention in recent years due to their high nutritional value and sustainable agricultural practices. This abstract provides an overview of millets, their processing methods, and their remarkable nutritional benefits, highlighting their potential to address global food and nutrition challenges. Millets have been cultivated for th...

U. Meghana, S. Krupa and C. D. Vandana *

Department of Biotechnology and Genetics, School of Sciences, Jain (Deemed-to-be University), Bangalore, Karnataka, India.

DOI: 10.13040/IJPSR.0975-8232.15(8).2230-43

Research Articles

The concentration/response function behaviorsof noncaloric and caloric sweeteners.

Sweeteners are generally described in terms of their sweetness potencies relative to sucrose references. And, while caloric sweeteners have been found to have potencies invariant with sucrose reference concentration, noncaloric sweeteners decrease in potency as sucrose reference concentration increases. In this study, we develop methodology to determine the Concentration/Response functions for 9 (...

Grant E. DuBois, Rafael I. San Miguel, B. Thomas Carr, Karen Wilkens, Allison Bechman and Indra Prakash *

Flavor & Ingredient Research, The Coca-Cola Company, One Coca-Cola Plaza, Atlanta, GA 30313, USA.

DOI: 10.13040/IJPSR.0975-8232.15(8).2289-95

EX-VIVO STUDIES OF THE EFFECT OF WHEATGRASS ON GLUCOSE RELEASE AND GLUCONEOGENESIS USING CORTISOL-INDUCED HEPATOCYTES

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PERSPECTIVE article

Artificial intelligence integration in the drug lifecycle and in regulatory science: policy implications, challenges and opportunities.

• 1 Agence Nationale de Sécurité des Médicaments et des Produits de Santé (ANSM) Saint-Denis, Saint-Denis, France
• 2 INSERM, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé, LIMICS, Sorbonne Université, Paris, France
• 3 France Assoc Santé, Paris, France
• 4 Faculty of Pharmacy of Lisbon University, Lisbon, Portugal
• 5 CHRC – Comprehensive Health Research Center, Evora, Portugal
• 6 EA 7379, Faculté de Santé, Université Paris-Est Créteil, Créteil, France
• 7 CHI Créteil, Créteil, France
• 8 Université de Versailles St Quentin-Paris Saclay, Inserm U1018, Guyancourt, France

Artificial intelligence tools promise transformative impacts in drug development. Regulatory agencies face challenges in integrating AI while ensuring reliability and safety in clinical trial approvals, drug marketing authorizations, and post-market surveillance. Incorporating these technologies into the existing regulatory framework and agency practices poses notable challenges, particularly in evaluating the data and models employed for these purposes. Rapid adaptation of regulations and internal processes is essential for agencies to keep pace with innovation, though achieving this requires collective stakeholder collaboration. This article thus delves into the need for adaptations of regulations throughout the drug development lifecycle, as well as the utilization of AI within internal processes of medicine agencies.

Introduction

The healthcare landscape has recently witnessed a proliferation of AI applications, many of which have found practical implementation through medical devices. These applications span various medical specialties, including radiology ( Samala et al., 2016 ), dermatology ( Esteva et al., 2017 ), ophthalmology ( Abràmoff et al., 2018 ), pathology ( Litjens et al., 2016 ), genome interpretation ( Kamps et al., 2017 ), biomarker discovery ( Diaz-Uriarte et al., 2022 ), and drug shortage studies ( Pall et al., 2023 ). It is worthy to note, however, that for applications like radiology, for instance, the use of AI is still far from routine, and needs dedicated teams and skills ( Shelmerdine et al., 2024 ). Furthermore, AI is making new inroads into clinical trial processes ( EMA, 2022 ), with the recent milestone of the first wholly AI-designed drug ( Chace, 2024 ). Although still in its nascent stages, the theoretical potential of AI in pharmaceutical product development is vast, spanning from rational drug design and decision-making support to personalized medication and clinical data management ( Duch et al., 2007 ; Blasiak et al., 2020 ; D Amico et al., 2023 ). Consequently, AI tools and applications are poised to play an increasingly pivotal role across all stages of the drug lifecycle, including drug discovery, manufacturing, nonclinical testing, clinical research, and surveillance ( Harrer et al., 2019 ; Gupta et al., 2021 ; Hauben and Hartford, 2021 ; Kang et al., 2023 ). This review elucidates the profound regulatory implications of AI’s existing or potential involvement in pharmaceutical product development at every stage of the drug lifecycle, particularly in relation to the body of evidence utilized for clinical trials and marketing authorization. As regulatory agencies are tasked with ensuring the quality, safety, and efficacy of medicinal drugs and are at the forefront of assessing these evolving methodologies, the overarching aim of this paper is to comprehensively explore the potential spectrum of AI applications in drug-related regulatory science with proposals for actionable regulatory recommendations. Additionally, this paper reviews the potential of AI to enhance and optimize regulatory processes at regulatory agencies concerning drug assessment, authorization, and post-authorization surveillance.

We will first give an overview of existing or potential AI applications in the drug lifecycle, with step-specific questions about the data and models used and the corresponding regulatory challenges and policy implications. In a second part, we will propose regulatory recommendations or adaptations that may be required to meet those challenges. In a third part, we will show how AI may help optimize and expedite internal regulatory agencies’ processes, to the benefit of patients. We hope that this perspective will contribute to accelerating relevant future regulatory adaptations and understanding among all stakeholders in the field of AI use in the drug lifecycle.

Policy implications regarding stepwise AI applications in the drug lifecycle

The potential uses of AI are outlined here across different phases of the drug life cycle, from drug discovery to clinical trials and post-authorization activities.

AI algorithms are widely applied for drug discovery ( Burki, 2019 ; Vamathevan et al., 2019 ). Quantitative structure-activity/property relationship (QSAR/QSPR), as well as structure-based modeling, new molecule design, and synthesis prediction, may be addressed by AI ( Jiménez-Luna et al., 2021 ; Paul et al., 2021 ; Vora et al., 2023 ). Computational methods have been used for a long time for ligand-binding probability calculations ( Fujita and Winkler, 2016 ) and for ADMET (absorption, distribution, metabolism, and toxicity) prediction ( Norinder and Bergström, 2006 ; Beck and Geppert, 2014 ). Several pharmaceutical companies are currently working with AI organizations (such as companies and research laboratories) along different lines ( Paul et al., 2021 ). Recently, the first wholly AI-designed drug entered clinical trials ( Chace, 2024 ). During the development of this new drug, TRAF2- and NCK-interacting kinase (TNIK) was first identified as an anti-fibrotic target using a predictive artificial intelligence (AI) approach (using PandaOmics ( Kamya et al., 2024 )). Then, using generative AI [Chemistry42 ( Ivanenkov et al., 2023 )], a small-molecule TNIK inhibitor was designed ( Ren et al., 2024 ). This drug entered two phase I studies in 18 months, from target discovery to preclinical candidate, including traditional testing in animal models, which is a very short timeline. Regarding timelines and costs, it is usually around 5.5–14.5 years (or more for target discovery) without the AI approach to reach the preclinical stage. In terms of costs, the traditional approach costs around 674 million dollars for a preclinical candidate, whereas it is much lower with the AI approach ( Pun et al., 2024 ). The application of AI in drug screening could reduce R&D costs by 50% while increasing efficiency and accuracy ( Wang et al., 2019 ). As another example of a state-of-the-art recent AI application for drug discovery, AlphaFold allows predicting protein structures at the atomic level, potentially accelerating drug discovery in cancer research ( Abramson et al., 2024 ; Xu et al., 2024 ). Nevertheless, even if all these technologies and their potentials seem impressive, most are at preliminary stages, there are few success stories, and it still remains to be determined if AI will really perform better and faster to develop more and more new successful drug candidates ( Schneider et al., 2020 ; Bender and Cortés-Ciriano, 2021 ). Moreover, till now, in the cases reviewed above, preclinical validation was carried out in traditional animal models.

In addition to potentially helping predict toxicity of drug candidates, AI approaches in preclinical testing can contribute to replacing, reducing, and refining the use of animals ( Luechtefeld et al., 2018 ). This second incentive is quite powerful. As in drug discovery, large amounts of toxicological data already exist and can be used to construct AI tools that are relevant for toxicity prediction ( Mayr et al., 2016 ; Luechtefeld et al., 2018 ; Lysenko et al., 2018 ; Basile et al., 2019 ; Wu et al., 2021 ). Non-animal approaches (such as QSAR, read-across, PB/PK, metabolomics, and cell painting, to cite just a few) rely as well on big toxicological, biological, and chemical data ( Bray et al., 2016 ; Luechtefeld et al., 2018 ; Liu et al., 2023 ), for which quality should be thoroughly checked and ensured before training any prediction model, given that new kinds of toxicity cannot always be derived from previously learned ones (reliance solely on historical toxicology data might not be sufficient in several cases).

In the future, AI tools might be used for improving clinical trials with digital twins and optimizing the control arms ( EMA, 2022 ; Fountzilas et al., 2022 ; Askin et al., 2023 ). They might help in patient selection and monitoring (eligibility, suitability, motivation, empowerment, adherence, and retention), thereby increasing clinical trials’ success rates ( Harrer et al., 2019 ). They could also participate in designing more relevant trials, especially for precision medicine (for a review, see ( Fountzilas et al., 2022 )). Patient selection is the area where AI could be most used, followed by trial design (two times less) and analysis (three times less) ( Askin et al., 2023 ). Overall, it is the mass and diversity of data that AI can process that could make the difference. Biomedical data from different origins (such as health insurance medical records, hospitals, genomics, biobanks, and radiology) may indeed be used to improve the enrolment and the design and follow-up of clinical trials ( Acosta et al., 2022 ). It is also used to generate synthetic clinical data (synthetic patients) for accelerating precision medicine, increasing the coverage of the population involved in the clinical trial ( Yu et al., 2018 ; EMA, 2022 ).

AI may be used to improve quality-by-design approaches ( Rantanen and Khinast, 2015 ; Manzano et al., 2021 ). This includes tools to deal with the interpretation of experimental big data from various sources, such as real-time process control and real-time quality assurance ( Hussain et al., 1991 ; Takayama et al., 1999 ; Rantanen and Khinast, 2015 ).

Pharmacovigilance (PV) is a data-driven field because it necessitates the gathering, processing, and analysis of significant amounts of data from a variety of very different sources ( Carbonell et al., 2015 ). Here, AI techniques may be used for signal detection, data intake, or analysis ( Hauben and Hartford, 2021 ). In practice, it is used and recommended mostly for signal detection and processing before data intake ( Ball and Dal Pan, 2022 ; Martin et al., 2022 ). Industrials have reported the performance of several AI systems for signal detection and adverse event processing ( Schmider et al., 2019 ; Routray et al., 2020 ). One study showed that the use of safety database data fields with dedicated AI applications (artificial intelligence and robotic process automation) as a surrogate for otherwise time-consuming and costly direct annotation of source documents is viable and feasible ( Schmider et al., 2019 ). An example of an augmented AI system with a neural network approach used for an accurate and scalable solution for pharmacovigilance determination of adverse event seriousness in spontaneous, solicited, and medical literature reports was published ( Routray et al., 2020 ). Data from a wide variety of sources can theoretically be used, including real world data such as electronic healthcare records (EHR) or social media ( Comfort et al., 2018 ; Ball and Dal Pan, 2022 ; Actualité, 2024 ). AI can also be used for finer drug misuse detection ( Afshar et al., 2022 ).

Actionable recommendations

Regulatory agencies and stakeholders’ information needs- transparency & explainability.

In the fast-paced world of drug development, transparency is a cornerstone of trust and accountability ( Transparency, 2024 ). When it comes to the application of artificial intelligence (AI), transparency becomes even more crucial ( Crossnohere et al., 2022 ). In this respect, it is of utmost importance that stakeholders – especially regulators –, have access to clear information about the AI models driving drug development. Goals, data used, intended applications, advantages, and drawbacks of AI models should be clear so that everyone understands how they fit into each specific drug development. Regulators need this level of transparency and explainability to assess accuracy, precision, limitations, and uncertainties effectively ( Hicks et al., 2022 ). One of the answers is therefore explainable AI ( Alizadehsani et al., 2024 ). This is a recent discipline by itself (xAI). Several mathematical techniques are used to render AI methods and results more easy to understand (reviewed in ( Holzinger et al., 2022 )). In this respect, techniques like SHapley Additive exPlanations (SHAP) and, Local Interpretable Model-agnostic Explanations (LIME), Integrated Gradients, and Counterfactual Explanations offer windows into the black box of AI decision-making, providing clarity on the processes behind the algorithms ( Mertes et al., 2022 ; Kırboğa et al., 2023 ; Wang et al., 2024 ). And transparency doesn't end once the model is built. Since AI models may evolve, regulators need to stay in the loop on updates and changes, ensuring ongoing monitoring of their performance and impact.

In summary, transparency is about empowering regulatory agencies to acquire all the information they need to make informed decisions. This is the first condition for regulators to be able to assess AI use in drug development. They nevertheless have, of course also to take further actions to keep on ensuring the safety of patients treated with drugs in which AI has been used during one or several steps of their development. Since data, models and applications utilized when applying AI tools depend on the drug lifecycle step ( Figure 1A ), we propose here stepwise regulatory actions or adaptations. We also show how AI may help optimize and expedite internal regulatory agencies’ processes, simplify review timelines, and improve efficiency while maintaining the highest safety standards ( Figure 1B ).

Figure 1 . Domains of potential or existing use of AI in the drug lifecycle and for internal regulatory agencies’ processes. (A) Already existing or potential AI uses at each step of the drug lifecycle from drug discovery to post-market surveillance which are currently developed by researchers from academia and industry. (B) Examples of AI applications existing or in development in medicine agencies (in clockwise order of complexity, starting from Integration of big data from various sources and file formats in databases with automated annotations ). These applications have the potential of enhancing and streamlining internal agencies’ processes. They are developed collaborating with expert AI research groups.

Challenges and corresponding proposals in adjusting to AI’s use across the drug lifecycle

For the whole drug lifecycle, EMA suggests a risk-based approach so that developers preemptively and proactively establish the risks that need to be monitored and/or mitigated ( EMA, 2023a ). The FDA and the MHRA address mainly the use of AI in medical devices and for digital health technologies (sensors, wearables, etc.) ( federalregister, 2023 ; MHRA, 2024b ; MHRA, 2024a ). Different centers within the FDA (CBER, CDER, CDRH, and OCP) also collaborate to leverage AI and other advanced technologies to enhance the regulation of medical products ( FDA, 2024b ). Overall, up to now, no regulatory recommendation proposal for drug development has been published. Stepwise (drug discovery, non-clinical and toxicity, translational and clinical research, pharmaceutical manufacturing, and pharmacovigilance), specific regulatory challenges are therefore delineated here, together with points to consider and possible future adaptations, which are presented in Table 1 .

Table 1 . Regulator’s considerations and possible regulatory actions by subject of potential interest in the different steps of the drug lifecycle.

In the race for innovative therapies, AI emerges as a powerful ally in drug discovery. Only one wholly AI-designed drug has entered clinical trials thus far ( Generative Artificial Intelligence for Drug Discovery, 2024 ), and regulatory agencies are not mandated to assess the methodologies used unless they contribute to the overall body of evidence. However, as AI models become integral to drug design, a dialogue between regulators and developers becomes imperative to ensure transparency and understanding of model performances as regards their predictions’ accuracy and reproducibility ( Table 1 A). Additionally, AI holds promise for accelerating drug repurposing efforts, leveraging big data analysis to identify new medical indications for existing drugs with unprecedented speed and precision ( Zong et al., 2022 ).

In regulatory science, and specifically in non-clinical testing and toxicity prediction, AI tools have great potential to predict safety outcomes, but their suitability remains to be determined. First of all, these tools offer a promising avenue to potentially reduce or even replace the traditional reliance on animal testing, which is a powerful incentive ( EMA, 2023a ). Notably, the FDA Modernization Act 2.0 in the United States takes a stride forward by curbing the mandatory use of animal models for toxicity predictions ( Han, 2023 ). AI non-clinical models draw from a rich diversity of data sources—from in vitro and in vivo experiments to expansive databases—employing diverse algorithms and machine learning techniques ( Maertens et al., 2022 ). Toxicity predictions generated using AI (machine learning on relevant biological, chemical, or toxicological data) are inherently probabilistic and contingent upon the quality and quantity of the input data, but they have great potential ( Mayr et al., 2016 ; Wu et al., 2021 ; Maertens et al., 2022 ). However, rigorous assessment of the data and models used and potential adjustments to regulatory frameworks will be necessary in the long run ( Table 1 B) ( Paul et al., 2021 ). Several efforts have been made or are underway to curate and reliably annotate toxicological databases ( Lea et al., 2017 ; Nair et al., 2020 ; Wu et al., 2023 ).

In translational and clinical research, several regulatory projects regarding AI use are currently led by the FDA and EMA ( EMA, 2023a ; FDA, 2024c ), underscoring the burgeoning potential of AI in these domains. During the COVID-19 pandemic, AI played a crucial role in accelerating vaccine trials. Companies like Moderna and Pfizer used AI to design trials, monitor patient data, and streamline regulatory submissions. AI tools helped identify suitable trial participants more quickly, designed adaptive trial protocols that adjusted in real-time based on interim results, and monitored adverse events to ensure participant safety. This use of AI contributed to the unprecedented speed at which COVID-19 vaccines were developed and approved (reviewed in ( Sharma et al., 2022 )). However, the current absence of regulations in this domain raises pertinent questions, highlighting the pressing need for new oversight ( Arora and Arora, 2022 ). Take, for instance, the digitization of clinical trials—an innovative approach leveraging data from electronic health records (EHR), routine medical exams, and various diagnostic tests. This digital transformation not only streamlines patient selection but also opens doors for broader trial participation. Yet, navigating the complexities of data management in these trials necessitates transparency in AI algorithms ( Kasahara et al., 2024 ), and several open questions remain ( Table 1 C).

In drug manufacturing, AI tools are also revolutionizing various aspects, from process design and scaling up to advanced control and fault detection. Both the FDA and EMA are actively crafting recommendations in this domain ( EMA, 2023a ; FDA, 2024a ). While the full extent of AI’s impact is yet to be realized (consultations are ongoing), it’s evident that the field is rapidly expanding ( Table 1 D). Given that these techniques primarily originate in industrial sectors, fostering closer collaboration between manufacturers and regulators is imperative. Notably, the real-time application of these methods on the factory floor poses unique challenges, necessitating robust regulatory frameworks and onsite inspections for compliance.

In pharmacovigilance, AI is gaining traction as a potent tool for enhancing drug safety monitoring. The EMA’s reflection paper acknowledges its significance, while the FDA’s discussion paper delineates its role across case processing, evaluation, and automated submissions prior to individual safety report submissions ( EMA, 2023a ; FDA, 2024c ). In pharmacovigilance, regulators already take advantage of AI techniques to better deal with big data from various sources. Pharmacovigilance is therefore the field in which the use of AI is now most mastered and is currently used by regulatory agencies ( Martin et al., 2022 ; Routray et al., 2020 ; Actualité, 2024 ), which may and should establish collaborations with academic research laboratories to use AI for specific projects with low-level or early detection signals.

More specifically, AI is used here for improving data analysis from institutional databases (World Health Organization’s Vigibase, EMA’s Eudravigilance, FDA’s Adverse Event Reporting System (FAERS), etc.) by developing AI algorithms better than the classic statistical ones. AI may also be used for improving data quality in databases (symbolic AI), allowing better groupings before analysis, increasing the number of cases by developing AI tools to collect more data from physicians or patients; or using other sources (EHR or social media) ( Table 1 E).

Another key aspect is that as the landscape of drug development evolves, it’s becoming increasingly clear that regulatory agencies will need to bolster their expertise in AI. This demand for AI-specific skills varies depending on the stage of drug development, so specific stepwise upskilling of assessors will be required. Indeed, the evaluation of the specific data, applications, and models used will be needed for preparing corresponding scientific assessment reports (see specific data sources and applications in Table 1 ).

Seizing AI opportunities: optimizing regulatory processes

Regulatory agencies have to deal with amounts and sources of data that are increasingly diverse and massive (raw data reports, real-world data, images, tables, EHR, etc.). Beside the drug lifecycle, AI applications would also increasingly find their place in regulatory assessments ( Figure 1B ). Recent published advances come from the EMA ( Jornet, 2024 ). First, natural language processing and optical character recognition tools may be used to annotate, extract, and categorize relevant data from various sources submitted to these agencies (files for clinical trials and marketing authorizations, including text, tables, and images). The output will be implemented in AI-amenable databases. An application could then assess the contents and notify the relevant assessors. This would save time, improve reproducibility, and reduce errors (sparing humans low-added value and repetitive tasks). EMA uses AI to support the validation of variations by flagging missing documents, detecting dissimilarities, and automatically identifying changes. AI tools can also find personal data, compare documents, do triage, and perform automated literature reviews. At the European level, there are also several other projects aimed at challenging and furthering AI use in a regulatory setting. ( EMA, 2023b ; bundesgesundheitsministerium, 2024 ; Regulatorische, 2024 ). A new NLP approach for harmonization of European medicinal product information has also recently been published ( Bergman et al., 2022 ).

In the regulatory setting, AI tools may be used to categorize and annotate texts from various sources and help implement progressively a collective memory to compare files, perform pre-analyses, and produce knowledge graphs (making comparisons easier). They could also, theoretically, help as regulatory assessment assistants in the near future.

Perspectives and conclusion

Today, it is quite difficult to gather accurate data on AI use in the lifecycle of drugs in published data except at the clinical trial stage. This shows that information about AI use for health topics or in health products is not readily and easily accessible. The first major factor for relevant assessment by regulators of these tools and applications is transparency, which goes with explainability using relevant tools [see above and ( Lundberg and Lee, 2017 )]. The second will be adapting current recommendations for developing new regulatory guidelines for AI use in the healthcare setting and collaborating with researchers, physicians, and the industry to improve the relevance of these guidelines. This could foster transparency, which regulators, the public, and health professionals demand ( Vellido, 2019 ). Other factors that have to be considered are the inherent complexity of AI models and their “black-box” nature ( Rudin, 2019 ) and concerns about data privacy and security ( Price, 2017 ). These are the main factors that may question stakeholders, such as patients and the general public. The international conference on harmonization (ICH) has recently addressed the use of AI and modeling for some topics related to the quality of drugs (e.g., product dissolution and in vivo / in vitro relationships/correlations, purge and fate of impurities, container/closure integrity, etc.) ( ICH, 2021 ), which would impact the ICH M7 guideline. The ICH M15 concept paper, “Model-Informed Drug Development General Principles Guideline,” also considers future approaches such as machine learning and AI ( M15, 2022 ). The ICH is therefore already considering the use of AI in drug development.

Applications of AI for internal processes at agencies also have great potential. To this end, there is important work to be done at regulatory agencies for selecting and validating the data incremented in the databases that will be useable to create the collective memory to be used for better and quicker assessments. In any case, this will require both collaboration with AI academic research laboratories, and acquiring internal competencies.

As we look into the future of drug regulation amidst the burgeoning era of AI, several critical questions subsist.

First of all, it remains to be determined what concrete steps can be taken to ensure that all stakeholders are involved (including patient associations in the larger frame of health democracy) in the utilization of AI tools in drug development. This health democracy setting will be important to better take into account potential ethical issues, such as potential patient selection and monitoring of clinical study biases. Another important point for the future will be to determine how regulatory bodies can navigate the complexities of AI models, while ensuring all corresponding ethical aspects. The ethical implications of using AI in drug development, include potential bias in AI models, informed consent, and patient autonomy. The use of AI may also raise privacy concerns like data privacy issues, data security, patient confidentiality, and compliance with regulations like GDPR. Potential solutions to these concerns are to design strategies like implementing robust data anonymization techniques, ensuring diverse and representative data sets to reduce bias, involving all stakeholders (including patient representatives), establishing transparent AI model validation processes (transparency, explainability), and adhering to local and international ethical guidelines and frameworks. Looking ahead, international collaboration among regulatory authorities will be instrumental in developing common responses and standards for evaluating AI technologies in pharmaceutical development. By harnessing the collective expertise and resources of global stakeholders, regulators should forge an adaptive framework that fosters transparency, innovation, and patient-centric outcomes.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Author contributions

WO-G: Conceptualization, Methodology, Project administration, Supervision, Validation, Writing–original draft, Writing–review and editing. M-CJ: Conceptualization, Methodology, Validation, Writing–review and editing. J-PT: Validation, Writing–review and editing. SO-M: Validation, Writing–review and editing. LB: Investigation, Writing–review and editing. PM: Supervision, Validation, Writing–review and editing. JA: Supervision, Validation, Writing–review and editing.

Members of The Scientific Advisory Board of ANSM

Joël Ankri (Chairman), Janine Barbot, Robert Barouki, Éric Bellissant, Patrick Castel, Patrick Chaskiel, Nicolas Clere, Christiane Druml, Sofia de Oliveira Martins, François Eisinger, Éric Ezan, Catherine Gourlay-France, Jamila Hamdani, Walter Janssens, Marie-Christine Jaulent, Maria Emilia Monteiro, Sylvie Odent, Fred Paccaud, Dominique Pougheon, Vololona Rabeharisoa, Victoria Rollason, Valérie Sautou, Jean-Pierre Thierry, Jean-Paul Vernant.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. All work was funded by ANSM.

Acknowledgments

The authors are very grateful to the following ANSM officers for their invaluable inputs: Malika Boussaid, Nicolas Delemer, Pierre Demolis, Vincent Gazin, David Morelle, Jean-Michel Race, Stéphane Vignot, and Mahmoud Zureik. We would also like to thank the following experts: Emmanuel Bacry, Arnaud Bayle, Catherine Duclos, Marco Fiorini, Julian Isla and Xavier Tannier.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., and Folk, J. C. (2018). Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. Npj Digit. Med. 1 (1), 39. doi:10.1038/s41746-018-0040-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., et al. (2024). Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630 (8016), 493–500. doi:10.1038/s41586-024-07487-w

Acosta, J. N., Falcone, G. J., Rajpurkar, P., and Topol, E. J. (2022). Multimodal biomedical AI. Nat. Med. 28 (9), 1773–1784. PMID:36109635. doi:10.1038/s41591-022-01981-2

Actualité (2024). Actualité - Lancement de la nouvelle application nationale de pharmacovigilance. ANSM. Available at: https://ansm.sante.fr/actualites/lancement-de-la-nouvelle-application-nationale-de-pharmacovigilance [Accessed November 3, 2023]

Google Scholar

Afshar, M., Sharma, B., Dligach, D., Oguss, M., Brown, R., Chhabra, N., et al. (2022). Development and multimodal validation of a substance misuse algorithm for referral to treatment using artificial intelligence (SMART-AI): a retrospective deep learning study. Lancet Digit. Health 4 (6), e426–e435. doi:10.1016/S2589-7500(22)00041-3

Alizadehsani, R., Oyelere, S. S., Hussain, S., Jagatheesaperumal, S. K., Calixto, R. R., Rahouti, M., et al. (2024). Explainable artificial intelligence for drug discovery and development: a comprehensive survey. IEEE Access 12, 35796–35812. doi:10.1109/ACCESS.2024.3373195

CrossRef Full Text | Google Scholar

Arora, A., and Arora, A. (2022). Synthetic patient data in health care: a widening legal loophole. Lancet 399 (10335), 1601–1602. doi:10.1016/S0140-6736(22)00232-X

Askin, S., Burkhalter, D., Calado, G., and El Dakrouni, S. (2023). Artificial Intelligence Applied to clinical trials: opportunities and challenges. Health Technol. 13 (2), 203–213. PMID:36923325. doi:10.1007/s12553-023-00738-2

Ball, R., and Dal Pan, G. (2022). “Artificial intelligence” for pharmacovigilance: ready for prime time? Drug Saf. N. Z. 45 (5), 429–438. PMID:35579808. doi:10.1007/s40264-022-01157-4

Basile, A. O., Yahi, A., and Tatonetti, N. P. (2019). Artificial intelligence for drug toxicity and safety. Trends Pharmacol. Sci. Engl. 40 (9), 624–635. PMID:31383376. doi:10.1016/j.tips.2019.07.005

Beck, B., and Geppert, T. (2014). Industrial applications of in silico ADMET. J. Mol. Model. 20 (7), 2322. PMID:24972798. doi:10.1007/s00894-014-2322-5

Bender, A., and Cortés-Ciriano, I. (2021). Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: ways to make an impact, and why we are not there yet. Drug Discov. Today 26 (2), 511–524. PMID:33346134. doi:10.1016/j.drudis.2020.12.009

Bergman, E., Sherwood, K., Forslund, M., Arlett, P., and Westman, G. (2022). A natural language processing approach towards harmonisation of European medicinal product information. PLOS ONE 17 (10), e0275386. doi:10.1371/journal.pone.0275386

Blasiak, A., Khong, J., and Kee, T. (2020). CURATE.AI: optimizing personalized medicine with artificial intelligence. SLAS Technol. 25 (2), 95–105. doi:10.1177/2472630319890316

Bray, M.-A., Singh, S., Han, H., Davis, C. T., Borgeson, B., Hartland, C., et al. (2016). Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat. Protoc. 11 (9), 1757–1774. doi:10.1038/nprot.2016.105

Bundesgesundheitsministerium (2024). Regulatorische Nutzung von Big Data-Strategien basierend auf OMICS-Daten zur effizienten Entwicklung, Zulassung und sicheren Anwendung von biologischen Arzneimitteln (RENUBIA). Available at: https://www.bundesgesundheitsministerium.de/ministerium/ressortforschung/handlungsfelder/digitalisierung/renubia (Accessed March 2, 2024).

Burki, T. (2019). Pharma blockchains AI for drug development. Lancet 393 (10189), 2382. doi:10.1016/S0140-6736(19)31401-1

Carbonell, P., Mayer, M. A., and Bravo, À. (2015). Exploring brand-name drug mentions on Twitter for pharmacovigilance. Stud. Health Technol. Inf. Neth. 210, 55–59. doi:10.3233/978-1-61499-512-8-55

Chace, C. (2024). First wholly AI-developed drug enters phase 1 trials. Forbes . Available at: https://www.forbes.com/sites/calumchace/2022/02/25/first-wholly-ai-developed-drug-enters-phase-1-trials/ [Accessed July 25, 2023].

Comfort, S., Perera, S., Hudson, Z., Dorrell, D., Meireis, S., Nagarajan, M., et al. (2018). Sorting through the safety data haystack: using machine learning to identify individual case safety reports in social-digital media. Drug Saf. N. Z. 41 (6), 579–590. PMID:29446035. doi:10.1007/s40264-018-0641-7

Crossnohere, N. L., Elsaid, M., Paskett, J., Bose-Brill, S., and Bridges, J. F. P. (2022). Guidelines for artificial intelligence in medicine: literature review and content analysis of frameworks. J. Med. Internet Res. 24 (8), e36823. doi:10.2196/36823

D Amico, S., Dall’Olio, D., Sala, C., Dall’Olio, L., Sauta, E., Zampini, M., et al. (2023). Synthetic data generation by artificial intelligence to accelerate research and precision medicine in hematology. JCO Clin. Cancer Inf. 7 (7), e2300021. doi:10.1200/CCI.23.00021

Diaz-Uriarte, R., Gómez de Lope, E., Giugno, R., Fröhlich, H., Nazarov, P. V., Nepomuceno-Chamorro, I. A., et al. (2022). Ten quick tips for biomarker discovery and validation analyses using machine learning. PLoS Comput. Biol. 18 (8), e1010357. PMID:35951526. doi:10.1371/journal.pcbi.1010357

Duch, W., Swaminathan, K., and Meller, J. (2007). Artificial intelligence approaches for rational drug design and discovery. Curr. Pharm. Des. 13 (14), 1497–1508. doi:10.2174/138161207780765954

EMA (2022). Qualification opinion for prognostic covariate adjustment (PROCOVA TM ). Available at: https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/qualification-opinion-prognostic-covariate-adjustment-procovatm_en.pdf .

EMA (2023a). Reflection paper on the use of artificial intelligence in lifecycle medicines . USA: Eur Med Agency . Available at: https://www.ema.europa.eu/en/news/reflection-paper-use-artificial-intelligence-lifecycle-medicines (Accessed July 25, 2023).

Ema, H. M. A. (2023b). Clusters of excellence discussion paper. European commission.

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature 542 (7639), 115–118. doi:10.1038/nature21056

FDA (2024a). Discussion paper: artificial intelligence in drug manufacturing. FDA CDER Framew. Regul. Adv. Manuf. Eval . Available at: https://www.fda.gov/media/165743/download .

FDA (2024b). Artificial intelligence & medical products: how CBER, CDER, CDRH, and OCP are working together. Available at: https://www.fda.gov/media/177030/download .

FDA (2024c). Using artificial intelligence and machine learning in the development of drug and biological products. Available at: https://www.fda.gov/media/167973/ .

Federalregister Framework for the use of digital health technologies in drug and biological product development; availability. Fed. Regist. 2023. Available at: https://www.federalregister.gov/documents/2023/03/24/2023-06066/framework-for-the-use-of-digital-health-technologies-in-drug-and-biological-product-development [Accessed July 8, 2024].

Fountzilas, E., Tsimberidou, A. M., Vo, H. H., and Kurzrock, R. (2022). Clinical trial design in the era of precision medicine. Genome Med. 14 (1), 101. PMID:36045401. doi:10.1186/s13073-022-01102-1

Fujita, T., and Winkler, D. A. (2016). Understanding the roles of the “two QSARs.”. J. Chem. Inf. Model. 56 (2), 269–274. PMID:26754147. doi:10.1021/acs.jcim.5b00229

Generative Artificial Intelligence for Drug Discovery (2024). How the first AI-discovered and AI-designed drug progressed to phase 2 clinical testing | research communities by springer nature. Available at: https://communities.springernature.com/posts/draft-9b0556ab-aa4d-49fd-bf3c-9c1a621defff (Accessed May 22, 2024).

Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., and Kumar, P. (2021). Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol. Divers 25 (3), 1315–1360. PMID:33844136. doi:10.1007/s11030-021-10217-3

Han, J. J. (2023). FDA Modernization Act 2.0 allows for alternatives to animal testing. Artif. Organs 47 (3), 449–450. doi:10.1111/aor.14503

Harrer, S., Shah, P., Antony, B., and Hu, J. (2019). Artificial intelligence for clinical trial design. Trends Pharmacol. Sci. 40 (8), 577–591. PMID:31326235. doi:10.1016/j.tips.2019.05.005

Hauben, M., and Hartford, C. G. (2021). Artificial intelligence in pharmacovigilance: scoping points to consider. Clin. Ther. U. S. 43 (2), 372–379. PMID:33478803. doi:10.1016/j.clinthera.2020.12.014

Hicks, S. A., Strümke, I., Thambawita, V., Hammou, M., Riegler, M. A., Halvorsen, P., et al. (2022). On evaluation metrics for medical applications of artificial intelligence. Sci. Rep. 12 (1), 5979. doi:10.1038/s41598-022-09954-8

Holzinger, A., Saranti, A., Molnar, C., Biecek, P., and Samek, W. (2022). “Explainable AI methods - a brief overview,” in XxAI - explain AI . Editors A. Holzinger, R. Goebel, R. Fong, T. Moon, K.-R. Müller, and W. Samek (Cham: Springer International Publishing ), 13–38. doi:10.1007/978-3-031-04083-2_2

Hussain, A. S., Yu, X., and Johnson, R. D. (1991). Application of neural computing in pharmaceutical product development. Pharm. Res. 08 (10), 1248–1252. doi:10.1023/A:1015843527138

ICH (2021). FUTURE OPPORTUNITIES and MODERNIZATION OF ICH QUALITY GUIDELINES: IMPLEMENTATION OF THE ICH QUALITY VISION1 FROM THE ICH QUALITY REFLECTION PAPER ICH QUALITY DISCUSSION GROUP (2019-2021). Available at: https://database.ich.org/sites/default/files/ICH_QDG_Recommendation_2021_1012.pdf .

Ivanenkov, Y. A., Polykovskiy, D., Bezrukov, D., Zagribelnyy, B., Aladinskiy, V., Kamya, P., et al. (2023). Chemistry42: an AI-driven platform for molecular design and optimization. J. Chem. Inf. Model. 63 (3), 695–701. doi:10.1021/acs.jcim.2c01191

Jiménez-Luna, J., Grisoni, F., Weskamp, N., and Schneider, G. (2021). Artificial intelligence in drug discovery: recent advances and future perspectives. Expert Opin. Drug Discov. 16 (9), 949–959. PMID:33779453. doi:10.1080/17460441.2021.1909567

Jornet, J. B. (2024). AI and digitalisation at EMA .

Kamps, R., Brandão, R., Bosch, B., Paulussen, A., Xanthoulea, S., Blok, M., et al. (2017). Next-generation sequencing in oncology: genetic diagnosis, risk prediction and cancer classification. Int. J. Mol. Sci. 18 (2), 308. doi:10.3390/ijms18020308

Kamya, P., Ozerov, I. V., Pun, F. W., Tretina, K., Fokina, T., Chen, S., et al. (2024). PandaOmics: an AI-driven platform for therapeutic target and biomarker discovery. J. Chem. Inf. Model. 64 (10), 3961–3969. doi:10.1021/acs.jcim.3c01619

Kang, J., Chowdhry, A. K., Pugh, S. L., and Park, J. H. (2023). Integrating artificial intelligence and machine learning into cancer clinical trials. Semin. Radiat. Oncol. 33 (4), 386–394. doi:10.1016/j.semradonc.2023.06.004

Kasahara, A., Mitchell, J., Yang, J., Cuomo, R. E., McMann, T. J., and Mackey, T. K. (2024). Digital technologies used in clinical trial recruitment and enrollment including application to trial diversity and inclusion: a systematic review. Digit. Health 10, 20552076241242390. doi:10.1177/20552076241242390

Kırboğa, K. K., Abbasi, S., and Küçüksille, E. U. (2023). Explainability and white box in drug discovery. Chem. Biol. Drug Des. 102 (1), 217–233. doi:10.1111/cbdd.14262

Lea, I. A., Gong, H., Paleja, A., Rashid, A., and Fostel, J. (2017). CEBS: a comprehensive annotated database of toxicological data. Nucleic Acids Res. 45 (D1), D964-D971–D971. doi:10.1093/nar/gkw1077

Litjens, G., Sánchez, C. I., Timofeeva, N., Hermsen, M., Nagtegaal, I., Kovacs, I., et al. (2016). Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci. Rep. 6 (1), 26286. doi:10.1038/srep26286

Liu, A., Seal, S., Yang, H., and Bender, A. (2023). Using chemical and biological data to predict drug toxicity. SLAS Discov. 28 (3), 53–64. doi:10.1016/j.slasd.2022.12.003

Luechtefeld, T., Marsh, D., Rowlands, C., and Hartung, T. (2018). Machine learning of toxicological big data enables read-across structure activity relationships (RASAR) outperforming animal test reproducibility. Toxicol. Sci. Off. J. Soc. Toxicol. 165 (1), 198–212. PMID:30007363. doi:10.1093/toxsci/kfy152

Lundberg, S., and Lee, S.-I. (2017). A unified approach to interpreting model predictions. arXiv . doi:10.48550/ARXIV.1705.07874

Lysenko, A., Sharma, A., Boroevich, K. A., and Tsunoda, T. (2018). An integrative machine learning approach for prediction of toxicity-related drug safety. Life Sci. Alliance 1 (6), e201800098. PMID:30515477. doi:10.26508/lsa.201800098

M15 (2022). Model-informed drug development general principles guideline. Regulator’s considerations possible Regul. actions by Subj. potential interest Differ. steps drug lifecycle .

Maertens, A., Golden, E., Luechtefeld, T. H., Hoffmann, S., Tsaioun, K., and Hartung, T. (2022). Probabilistic risk assessment – the keystone for the future of toxicology. ALTEX 39 (1), 3–29. doi:10.14573/altex.2201081

Manzano, T., Fernàndez, C., Ruiz, T., and Richard, H. (2021). Artificial intelligence algorithm qualification: a quality by design approach to apply artificial intelligence in pharma. PDA J. Pharm. Sci. Technol. 75 (1), 100–118. PMID:32817323. doi:10.5731/pdajpst.2019.011338

Martin, G. L., Jouganous, J., Savidan, R., Bellec, A., Goehrs, C., Benkebil, M., et al. (2022). Validation of artificial intelligence to support the automatic coding of patient adverse drug reaction reports, using nationwide pharmacovigilance data. Drug Saf. N. Z. 45 (5), 535–548. PMID:35579816. doi:10.1007/s40264-022-01153-8

Mayr, A., Klambauer, G., Unterthiner, T., and Hochreiter, S. (2016). DeepTox: toxicity prediction using deep learning. Front. Environ. Sci. 3. Available from. doi:10.3389/fenvs.2015.00080

Mertes, S., Huber, T., Weitz, K., Heimerl, A., and André, E. (2022). GANterfactual—counterfactual Explanations for medical non-experts using generative adversarial learning. Front. Artif. Intell. 5, 825565. doi:10.3389/frai.2022.825565

MHRA (2024a). The emergence of artificial intelligence and machine learning algorithms in healthcare: recommendations to support governance and regulation - position paper. Available at: https://www.bsigroup.com/globalassets/localfiles/en-gb/about-bsi/nsb/innovation/mhra-ai-paper-2019.pdf .

MHRA (2024b). Impact of AI on the regulation of medical products Implementing the AI White Paper principles. Available at: https://assets.publishing.service.gov.uk/media/662fce1e9e82181baa98a988/MHRA_Impact-of-AI-on-the-regulation-of-medical-products.pdf .

Nair, S. K., Eeles, C., Ho, C., Beri, G., Yoo, E., Tkachuk, D., et al. (2020). ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets. Nucleic Acids Res. 48 (W1), W455-W462–W462. doi:10.1093/nar/gkaa390

Norinder, U., and Bergström, C. A. S. (2006). Prediction of ADMET properties. ChemMedChem 1 (9), 920–937. PMID:16952133. doi:10.1002/cmdc.200600155

Pall, R., Gauthier, Y., Auer, S., and Mowaswes, W. (2023). Predicting drug shortages using pharmacy data and machine learning. Health Care Manag. Sci. 26 (3), 395–411. doi:10.1007/s10729-022-09627-y

Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., and Tekade, R. K. (2021). Artificial intelligence in drug discovery and development. Drug Discov. Today 26 (1), 80–93. PMID:33099022. doi:10.1016/j.drudis.2020.10.010

Price, W. N. (2017). Regulating black-box medicine. Mich Law Rev. 116 (3), 421–474. PMID:29240330. doi:10.36644/mlr.116.3.regulating

Pun, F. W., Pulous, F., and Zhavoronkov, A. (2024). Generative artificial intelligence for drug discovery: how the first AI-discovered and AI-designed drug progressed to phase 2 clinical testing. Res. Communities Springer Nat . Available at: https://communities.springernature.com/posts/draft-9b0556ab-aa4d-49fd-bf3c-9c1a621defff [Accessed July 9, 2024].

Rantanen, J., and Khinast, J. (2015). The future of pharmaceutical manufacturing sciences. J. Pharm. Sci. 104 (11), 3612–3638. doi:10.1002/jps.24594

Regulatorische (2024). Regulatorische Nutzung KI-gestützter Methoden zur effizienten Bewertung und Regulation biomedizinischen Arzneimitteln (KIMERBA). Available at: https://www.bundesgesundheitsministerium.de/ministerium/ressortforschung/handlungsfelder/digitalisierung/kimerba (Accessed March 2, 2024).

Ren, F., Aliper, A., Chen, J., Zhao, H., Rao, S., Kuppe, C., et al. (2024). A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models. Nat. Biotechnol. 8. doi:10.1038/s41587-024-02143-0

Routray, R., Tetarenko, N., Abu-Assal, C., Mockute, R., Assuncao, B., Chen, H., et al. (2020). Application of augmented intelligence for pharmacovigilance case seriousness determination. Drug Saf. N. Z. 43 (1), 57–66. PMID:31605285. doi:10.1007/s40264-019-00869-4

Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1 (5), 206–215. doi:10.1038/s42256-019-0048-x

Samala, R. K., Chan, H., Hadjiiski, L., Helvie, M. A., Wei, J., and Cha, K. (2016). Mass detection in digital breast tomosynthesis: deep convolutional neural network with transfer learning from mammography. Med. Phys. 43 (12), 6654–6666. doi:10.1118/1.4967345

Schmider, J., Kumar, K., LaForest, C., Swankoski, B., Naim, K., and Caubel, P. M. (2019). Innovation in pharmacovigilance: use of artificial intelligence in adverse event case processing. Clin. Pharmacol. Ther. U. S. 105 (4), 954–961. PMID:30303528. doi:10.1002/cpt.1255

Schneider, P., Walters, W. P., Plowright, A. T., Sieroka, N., Listgarten, J., Goodnow, R. A., et al. (2020). Rethinking drug design in the artificial intelligence era. Nat. Rev. Drug Discov. 19 (5), 353–364. PMID:31801986. doi:10.1038/s41573-019-0050-3

Sharma, A., Virmani, T., Pathak, V., Sharma, A., Pathak, K., Kumar, G., et al. (2022). Artificial intelligence-based data-driven strategy to accelerate research, development, and clinical trials of COVID vaccine. Biomed. Res. Int. U. S. 2022, 7205241. PMID:35845955. doi:10.1155/2022/7205241

Shelmerdine, S. C., Togher, D., Rickaby, S., and Dean, G. (2024). Artificial intelligence (AI) implementation within the national health service (NHS): the south west London AI working group experience. Clin. Radiol . doi:10.1016/j.crad.2024.05.018

Takayama, K., Fujikawa, M., and Nagai, T. (1999). Artificial neural network as a novel method to optimize pharmaceutical formulations. Pharm. Res. 16 (1), 1–6. doi:10.1023/A:1011986823850

Transparency (2024). Transparency . USA: European Medicines Agency . Available at: https://www.ema.europa.eu/en/about-us/how-we-work/transparency (Accessed May 22, 2024).

Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., et al. (2019). Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov. 18 (6), 463–477. PMID:30976107. doi:10.1038/s41573-019-0024-5

Vellido, A. (2019). Societal issues concerning the application of artificial intelligence in medicine. Kidney Dis. 5 (1), 11–17. doi:10.1159/000492428

Vora, L. K., Gholap, A. D., Jetha, K., Thakur, R. R. S., Solanki, H. K., and Chavda, V. P. (2023). Artificial intelligence in pharmaceutical Technology and drug delivery design. Pharmaceutics 15 (7), 1916. doi:10.3390/pharmaceutics15071916

Wang, L., Ding, J., Pan, L., Cao, D., Jiang, H., and Ding, X. (2019). Artificial intelligence facilitates drug design in the big data era. Chemom. Intell. Lab. Syst. 194, 103850. doi:10.1016/j.chemolab.2019.103850

Wang, Y., Zhang, T., Guo, X., and Shen, Z. (2024). Gradient based feature attribution in explainable AI: a technical review. arXiv . doi:10.48550/ARXIV.2403.10415

Wu, L., Huang, R., Tetko, I. V., Xia, Z., Xu, J., and Tong, W. (2021). Trade-off predictivity and explainability for machine-learning powered predictive toxicology: an in-depth investigation with Tox21 data sets. Chem. Res. Toxicol. 34 (2), 541–549. PMID:33513003. doi:10.1021/acs.chemrestox.0c00373

Wu, L., Yan, B., Han, J., Li, R., Xiao, J., He, S., et al. (2023). TOXRIC: a comprehensive database of toxicological data and benchmarks. Nucleic Acids Res. 51 (D1), D1432–D1445. doi:10.1093/nar/gkac1074

Xu, J., Qi, H., Wang, Z., Wang, L., Steurer, B., Cai, X., et al. (2024). Discovery of a novel and potent cyclin-dependent kinase 8/19 (CDK8/19) inhibitor for the treatment of cancer. J. Med. Chem. 67 (10), 8161–8171. doi:10.1021/acs.jmedchem.4c00248

Yu, K.-H., Beam, A. L., and Kohane, I. S. (2018). Artificial intelligence in healthcare. Nat. Biomed. Eng. 2 (10), 719–731. PMID:31015651. doi:10.1038/s41551-018-0305-z

Zong, N., Wen, A., Moon, S., Fu, S., Wang, L., Zhao, Y., et al. (2022). Computational drug repurposing based on electronic health records: a scoping review. Npj Digit. Med. 5 (1), 77. doi:10.1038/s41746-022-00617-6

Keywords: artificial intelligence, health policy, regulatory science, drug lifecycle, drug approval process, patient safety

Citation: Oualikene-Gonin W, Jaulent M-C, Thierry J-P, Oliveira-Martins S, Belgodère L, Maison P, Ankri J and The Scientific Advisory Board of ANSM (2024) Artificial intelligence integration in the drug lifecycle and in regulatory science: policy implications, challenges and opportunities. Front. Pharmacol. 15:1437167. doi: 10.3389/fphar.2024.1437167

Received: 23 May 2024; Accepted: 18 July 2024; Published: 02 August 2024.

Reviewed by:

Copyright © 2024 Oualikene-Gonin, Jaulent, Thierry, Oliveira-Martins, Belgodère, Maison, Ankri and The Scientific Advisory Board of ANSM. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Wahiba Oualikene-Gonin, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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How to Cite The paper reviews an overview of a conventional and novel approach in the topical drug delivery system. Drug delivery via the skin is becoming progressively popular due to its convenience and affordability. The skin is the most important mechanical barrier to the penetration of many drug substances and acts as an ideal site to deliver the drug both locally and systemically. The topical route has been a favored route of drug administration over the last decades. Despite conventional topical drug delivery systems limits in poor retention and low bioavailability. This drawback overcomes by extensive research to develop a novel topical drug delivery system targeting to improve the safety, efficacy and to minimize side effects. The conventional review focuses on dusting powders, poultices, plasters, lotion, liniments, solution, emulsion, suspension, colloidions, tinctures, creams, gels, ointments, pastes, suppositories, transdermal delivery systems, tapes, and gauzes and rubbing alcohol while the novel review focuses on novel gels, aerosol foams, microsponges, muco-adhesive bio-adhesives, novel vesicular carriers, nano-emulsion & nano-emulgel, protein and peptide delivery, polymers, emulsifier-free formulations and fullerenes etc. The key purpose of a topical delivery system is to enhance the skin permeability and to retain in the dermis. This review addresses a basis for further advancement and up-gradation of current techniques and technologies. Full text article This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License . Article Details Similar articles. 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The Journal publishes original research work either as a Full Research Paper or as a Short Communication. Review Articles on a current topic in the said fields are also considered for publication by the Journal. Abstracting and Indexing : Google scholar, IndexCopernicus, DOAJ, PSOAR, Open-J-Gate, Indian Citation Index (ICI), OAI, LOCKKS, OCLC (World Digital Collection Gateway), UIUC. Scientific Journal Impact Factor-  4 . 510  (SJIF 2013 ) ICV (2011) – 4.70 Journals of Innovare Academic Sciences have made their presence in short span of time amongst International community with great impact ICV- 4.50, 4.28, 4.20, 3.38 . 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This journal is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0) . Licensed under www.ijrps.com | About this Publishing System.© 2023 Author Guidelines It encourages issues of vital importance to Pharmaceutical education and research. The goal of the journal is to provide quality publications and publish the most important research, review articles, case study report and short communication in the field of drug development, Pharmaceutical Sciences & allied sciences. International Journal of Research in Pharmaceutical Sciences This web page shows the SJR, documents, and citations per document of International Journal of Research in Pharmaceutical Sciences, a discontinued journal in Scopus. The journal had a low SJR and a declining number of publications and citations in the last years. International Journal of Research in Pharmaceutical Sciences International Journal of Research in Pharmaceutical Sciences | Read 1100 articles with impact on ResearchGate, the professional network for scientists. Online ISSN: 0975-7538 Share on Twitter International Journal of Research in Pharmaceutical Sciences International Journal of Research in Pharmaceutical Sciences. In the present study, a novel schiff bases were synthesized by condensation of 3-amino-2-methyl quinazolin-4- (3H)-ones with different ... International journal of research in pharmaceutical sciences ISSN 0975-7538 (Online) | International journal of research in pharmaceutical sciences. Skip to main content. Leave this field blank . Log In; Automatic login IP; PUBLISHERS' AREA DISCOVER ISSN SERVICES SEARCH OPEN ACCESS ... International journal of research in pharmaceutical sciences. Identifiers. ISSN : 0975-7538. Linking ISSN (ISSN-L): 0975 ... Authors Guidelines An open access peer-reviewed journal aiming to communicate high quality original research work that contribute scientific knowledge related to the field of Pharmaceutical Sciences. [email protected] ... In-vitro evaluation of antilthiatic activity of seeds of Dolichos biflorus and roots of Asparagus racemosus, International Journal of ... International Journal of Research in Pharmaceutical Sciences and Neliti is a platform for accessing and sharing research articles from various journals and disciplines. Find the latest publications from the International Journal of Research in Pharmaceutical Sciences and Technology, covering topics such as protein interactions, herbal formulations, artificial intelligence, and more. International Journal of Pharmaceutics The International Journal of Pharmaceutics is the third most cited journal in the "Pharmacy & Pharmacology" category out of 366 journals, being the true home for pharmaceutical scientists concerned with the physical, chemical and biological properties of devices and delivery systems for drugs, …. View full aims & scope. Research in Pharmaceutical Sciences May-Jun 2024 - Volume 19 - Issue 3. Editor-in-Chief: Prof. Jaber Emami. ISSN: 1735-5362. Online ISSN: 1735-9414. Frequency: Quarterly. Impact Factor: 2.1. eTOC Alert. Thought you might appreciate this item (s) I saw in Research in Pharmaceutical Sciences. Your message has been successfully sent to your colleague. International Journal of Pharmaceutical Sciences Pharmaceutical Sciences. An open access peer-reviewed journal aiming to communicate high quality original research work that contribute. scientific knowledge related to the field of Pharmaceutical Sciences. (ISO 9001:2015 Certified International Journal) ISSN: 0975-4725; CODEN (USA): IJPS00. Current Issue Publish with us. About the Journal International Journal of Research in Pharmaceutical Sciences (IJRPS) ISSN: 0975-7538 sponsored by JK Welfare & Pharmascope Foundation is established in the year 2009 with people from various avenues of the pharmacy profession. IJRPS is completely devoted to publications related to Pharmaceuticals and Health Sciences. International Journal of Research in Pharmaceutical Sciences The Impact IF 2020 of International Journal of Research in Pharmaceutical Sciences (discontinued) is 0.63, which is computed in 2021 as per its definition. International Journal of Research in Pharmaceutical Sciences (discontinued) IF is increased by a factor of 0.39 and approximate percentage change is 162.5% when compared to preceding year 2019, which shows a rising trend. Author Guidelines Research Papers: These are manuscripts predominantly devoted to reporting ORIGINAL research investigations in the pharmaceutical and biomedical sciences, including research in one of the core pharmaceutical sciences (pharmaceutics, biopharmaceutics, industrial pharmacy, clinical pharmacy, pharmacology and therapeutics, toxicology ... International Journal of Pharmaceutical Sciences and Research (IJPSR) IJPSR is a monthly online and print journal that publishes research articles on various topics in pharmaceutical sciences. It is indexed with many international databases and has a high impact factor. International Journal of Research in AYUSH and Pharmaceutical Sciences PUBLICATION CERTIFICATE. View All Issues. ISSN : 2456-9909 (Online) IJRAPS by is licensed under a Creative Commons Attribution-Non Commercial-Share Alike 4.0 International License. International Journal of Research in Pharmaceutical Sciences Abbreviation of International Journal of Research in Pharmaceutical Sciences. The ISO4 abbreviation of International Journal of Research in Pharmaceutical Sciences is Int. J. Res. Pharm. Sci. . It is the standardised abbreviation to be used for abstracting, indexing and referencing purposes and meets all criteria of the ISO 4 standard for abbreviating names of scientific journals. Namrata Satkar on LinkedIn: Excited to Announce My Recent Publication 🌟 Excited to Announce My Recent Publication in the International Journal of Pharmaceutical Sciences! 🌟 I am honored to share that my research paper titled "Formulation and Evaluation of ... Pharmaceutical technology journals The Journal of Drug Delivery Science and Technology is an international journal devoted to drug delivery and pharmaceutical technology. It is the Offical Journal of the Association de Pharmacie Galenique Industrielle (APGI), Associazione Docenti e Ricercatori Italiani di Tecnologie e Legislazione Farmaceutiche (ADRITELF), and Academy of Pharmaceutical Science and Technology, Japan (APSTJ).The ... International Journal of Research in Pharmaceutical and Nano Sciences The International Journal of Research in Pharmaceutical and Nano Sciences (IJRPNS) is an online Journal and international peer review Bi-monthly, scientific and professional journal emphasizing quality pharmaceutical discoveries and innovations. IJRPNS provides a medium for publication of novel and innovative research from the areas of ... Frontiers Figure 1.Domains of potential or existing use of AI in the drug lifecycle and for internal regulatory agencies' processes. (A) Already existing or potential AI uses at each step of the drug lifecycle from drug discovery to post-market surveillance which are currently developed by researchers from academia and industry.(B) Examples of AI applications existing or in development in medicine ... International Journal of Pharmaceutical Sciences Review and Research Our International Journal of Pharmaceutical Sciences Review and Research (IJPSRR) achieved a good index copernicus value in short span of time amongst International community with great impact. Elsevier-Scopus SCImago Impact factor 2016 = 0.65. Scopus SCImago Journal Rank (SJR) 2018: 0.18. Index copernicus value: ICV 2016: 92.89, ICV 2017: 71.72 Article Processing Charges (APC) International Journal of Research in Pharmaceutical Sciences (IJRPS) is an open access journal published by JK Welfare & Pharmascope Foundation, India, dedicated to encourage research in the field of Pharmaceutical and Health sciences. Due to costs towards article processing, maintenance of paper in secured data storage system, databases and ... An overview on topical drug delivery system Most read articles by the same author(s) Dandasi Jayachandra Dev, Jayaprakash J S, Kulkarni P K, Akhila A R, Namratha S Saraf, Formulation and evaluation of different topical dosage forms for wound healing properties , International Journal of Research in Pharmaceutical Sciences: Vol. 11 No. 1 (2020): Volume 11 Issue 1 International Journal of Current Pharmaceutical Research Welcome to IJCPR. The Journal publishes original research work that contributes significantly to further the scientific knowledge in pharmacy and pharmaceutical sciences (Pharmaceutical Technology, Pharmacognosy, Natural Product Research, Pharmaceutics, Novel Drug Delivery, Biopharmaceutics, Pharmacokinetics, Pharmaceutical/Medicinal Chemistry ... assignment essay homework paper phd report resume review thesis