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The human absorption, distribution, metabolism, and excretion (hADME) study is the cornerstone of the clinical pharmacology package for small molecule drugs, providing comprehensive information on the rates and routes of disposition and elimination of drug‐related material in humans through the use of 14C‐labeled drug. Significant changes have already been made in the design of the hADME study for many companies, but opportunity exists to continue to re‐think both the design and timing of the hADME study in light of the potential offered by newer technologies, that enable flexibility in particular to reducing the magnitude of the radioactive dose used. This paper provides considerations on the variety of current strategies that exist across a number of pharmaceutical companies and on some of the ongoing debates around a potential move to the so called "human first/human only" approach, already adopted by at least one company. The paper also provides a framework for continuing the discussion in the application of further shifts in the paradigm. [ABSTRACT FROM AUTHOR]

The landscape of oncology drug development has witnessed remarkable advancements over the last few decades, significantly improving clinical outcomes and quality of life for patients with cancer. Project Optimus, introduced by the U.S. Food and Drug Administration, stands as a groundbreaking endeavor to reform dose selection of oncology drugs, presenting both opportunities and challenges for the field. To address complex dose optimization challenges, an Oncology Dose Optimization IQ Working Group was created to characterize current practices, provide recommendations for improvement, develop a clinical toolkit, and engage Health Authorities. Historically, dose selection for cytotoxic chemotherapeutics has focused on the maximum tolerated dose, a paradigm that is less relevant for targeted therapies and new treatment modalities. A survey conducted by this group gathered insights from member companies regarding industry practices in oncology dose optimization. Given oncology drug development is a complex effort with multidimensional optimization and high failure rates due to lack of clinically relevant efficacy, this Working Group advocates for a case‐by‐case approach to inform the timing, specific quantitative targets, and strategies for dose optimization, depending on factors such as disease characteristics, patient population, mechanism of action, including associated resistance mechanisms, and therapeutic index. This white paper highlights the evolving nature of oncology dose optimization, the impact of Project Optimus, and the need for a tailored and evidence‐based approach to optimize oncology drug dosing regimens effectively. [ABSTRACT FROM AUTHOR]

The promise of viral vector‐based gene therapy (GT) as a transformative paradigm for treating severely debilitating and life‐threatening diseases is slowly coming to fruition with the recent approval of several drug products. However, they have a unique mechanism of action often necessitating a tortuous clinical development plan. Expertise in such complex therapeutic modality is still fairly limited in this emerging class of adeno‐associated virus (AAV) vector‐based gene therapies. Because of the irreversible mode of action and incomplete understanding of genotype–phenotype relationship and disease progression in rare diseases careful considerations should be given to GT product's benefit–risk profile. In particular, special attention needs to be paid to safe dose selection, reliable dose exposure response (including clinically relevant endpoints), or creative approaches in study design targeting small patient populations during clinical development. We believe that quantitative tools encompassed within model‐informed drug development (MIDD) framework fits quite well in the development of such novel therapies, as they enable us to benefit from the totality of data approach in order to support dose selection as well as optimize clinical trial designs, end point selection, and patient enrichment. In this thought leadership paper, we provide our collective experiences, identify challenges, and suggest areas of improvement in applications of modeling and innovative trial design in development of AAV‐based GT products and reflect on the challenges and opportunities for incorporating MIDD tools and more in rational development of these products. [ABSTRACT FROM AUTHOR]

Drug shortages continue at crisis levels in the United States, with no end in sight. Extensive research in disparate areas has been conducted to explore the impact that shortages have on patients and the healthcare system. We conducted a scoping review to categorize existing work in order to identify opportunities for further research. We considered peer‐reviewed and non‐peer‐reviewed articles involving non‐vaccine, human drug shortages in the United States published in English between January 2001 and May 2019. In total, 430 papers were charted according to the following categories: causes, impacts on care, health outcomes, costs, management, prevention, and federal government response. Of these, 112 papers considered causes; 199 discussed effects, 158 considered management strategies, and 140 discussed prevention. We provide a resource to navigate the vast literature on drug shortages in the United States, identifying areas in need of further research. This review highlights the widespread negative effects that drug shortages have on patients, providers, and health system costs in the United States. Evidence of their ramifications should be sufficient to justify policy change. Future work should move from characterizing the problem to working toward solutions to reduce the impact, occurrence, and effects of shortages. [ABSTRACT FROM AUTHOR]

Drug transporters are critically important for the absorption, distribution, metabolism, and excretion (ADME) of many drugs and endogenous compounds. Therefore, disruption of these pathways by inhibition, induction, genetic polymorphisms, or disease can have profound effects on overall physiology, drug pharmacokinetics, drug efficacy, and toxicity. This white paper provides a review of changes in transporter function associated with acute and chronic disease states, describes regulatory pathways affecting transporter expression, and identifies opportunities to advance the field. [ABSTRACT FROM AUTHOR]

The article presents a news brief for American Society for Clinical Pharmacology & Therapeutics (ASCPT) including information for a deadline for membership of ASCPT, campaign for the members to participate to know work of clinical pharmacology, and ASCPT's annual meeting on 6 September 2018. It also gives a deadline for abstract submission and presents a new member's and students or trainee's profile.

A news brief section is presented for the American Society for Clinical Pharmacology & Therapeutics (ASCPT) which includes the information for membership, registration details, and website information. It provides information to send abstracts of research articles, annual meeting registration, and detail information for conferences is being organized by the society.

The article provides an information of the issue which includes an information of editors of the issue, editorial board members, and publication. It discusses copyright and photocopying information for publication in the issue, subscription details, and information of American Society for Clinical Pharmacology & Therapeutics. It also presents a list of contents and research articles present in the issue.

I Clinical Pharmacology & Therapeutics i , B 111(S1) b : S5-S80 (2022). https://doi.org/10.1002/cpt.2521 In the original publication of the 2022 ASCPT Annual Meeting abstract supplement, the authors of P-163 noticed that the name of the research network is missing in the authors list. The authors list for P-163 should have been displayed as follows: J. Sinha SP 1 sp , K. Zimmerman SP 2 sp , S. Balevic SP 2 sp , W. Muller SP 3 sp , C. Hornik SP 2 sp , M. Rathore SP 4 sp , Y. Finkelstein SP 5 sp , J. Chen SP 6 sp , D. Lipscomb SP 6 sp , D. Gonzalez SP 1 sp on behalf of the Best Pharmaceuticals for Children Act Pediatric Trials Network Steering Committee. [Extracted from the article]

In the original publication of the 2021 ASCPT Annual Meeting abstract supplement, the figure included with abstract PIV-042, 'Application of Machine Learning (ML) to Predict Progression-Free Survival (PFS) in Advanced Renal Cell Carcinoma (RCC), ' was incorrect. Corrigendum: Abstracts of papers, 2021 Annual Meeting March 8-12, 15-21, 2021 and April 19-21, 2021. [Extracted from the article]

I Clinical Pharmacology & Therapeutics i , B 111(S1): b S5-S80 (2021). https://doi.org/10.1002/cpt.2521 In the original publication of the 2022 ASCPT Annual Meeting abstract supplement, we would like to correct the following: The abstracts P-024, P-046, P-047, P-120, P-121, P-143, P-177, P-191 are withdrawn In abstract P-061 there is change in affiliation linked with the author Ballesteros-Perez. In P-109, there is a change in the abstract title and it should be 'FACTORS INFLUENCING 4 -HYDROXYCHOLESTEROL PLASMA CONCENTRATIONS IN HUMANS' 5. In P-160, there is a change in author order and it should be displayed as follows: M. Fontes SP 1 sp , J. Dingemanse SP 1 sp , A. Halabi SP 2 sp , M. Tomaszewska-Kiecana SP 3 sp , P. Sidharta SP 1 sp. [Extracted from the article]

The article presents abstracts on medical topics which include a study on Acute lymphoblastic leukemia progression and various treatment options for it, hepatitis C treatment and its progression and treatment, and triple negative breast cancer and the cellular sensitivity of drug response to it.

Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered in a new era of possibilities across various scientific domains. One area where these advancements hold significant promise is model‐informed drug discovery and development (MID3). To foster a wider adoption and acceptance of these advanced algorithms, the Innovation and Quality (IQ) Consortium initiated the AI/ML working group in 2021 with the aim of promoting their acceptance among the broader scientific community as well as by regulatory agencies. By drawing insights from workshops organized by the working group and attended by key stakeholders across the biopharma industry, academia, and regulatory agencies, this white paper provides a perspective from the IQ Consortium. The range of applications covered in this white paper encompass the following thematic topics: (i) AI/ML‐enabled Analytics for Pharmacometrics and Quantitative Systems Pharmacology (QSP) Workflows; (ii) Explainable Artificial Intelligence and its Applications in Disease Progression Modeling; (iii) Natural Language Processing (NLP) in Quantitative Pharmacology Modeling; and (iv) AI/ML Utilization in Drug Discovery. Additionally, the paper offers a set of best practices to ensure an effective and responsible use of AI, including considering the context of use, explainability and generalizability of models, and having human‐in‐the‐loop. We believe that embracing the transformative power of AI in quantitative modeling while adopting a set of good practices can unlock new opportunities for innovation, increase efficiency, and ultimately bring benefits to patients. [ABSTRACT FROM AUTHOR]

This document provides abstracts of papers presented at the 2024 Annual Meeting of the American Society for Clinical Pharmacology and Therapeutics. The papers cover a wide range of topics related to clinical pharmacology and therapeutics, including the use of machine learning in predicting adverse events, the integration of brain- and blood-derived markers in antidepressant pharmacotherapy, and the use of NLP-powered topic modeling in drug repurposing. Other studies focused on the pharmacokinetics and safety of different drugs, the development of population pharmacokinetic models, and potential drug-drug interactions involving cannabidiol. These studies offer valuable insights into drug behavior and can help guide personalized treatment strategies in different populations. The document also discusses the use of computational tools and machine learning techniques to improve drug pharmacokinetics and clinical trial design. Overall, these studies contribute to our understanding of how drugs interact with the body and can inform more effective and personalized treatment approaches. [Extracted from the article]

Polygenic scores (PGSs) have emerged as promising tools for complex trait risk prediction. The application of these scores to pharmacogenomics provides new opportunities to improve the prediction of treatment outcomes. To gain insight into this area of research, we conducted a systematic review and accompanying analysis. This review uncovered 51 papers examining the use of PGSs for drug‐related outcomes, with the majority of these papers focusing on the treatment of psychiatric disorders (n = 30). Due to difficulties in collecting large cohorts of uniformly treated patients, the majority of pharmacogenomic PGSs were derived from large‐scale genome‐wide association studies of disease phenotypes that were related to the pharmacogenomic phenotypes under investigation (e.g., schizophrenia‐derived PGSs for antipsychotic response prediction). Examination of the research participants included in these studies revealed that the majority of cohort participants were of European descent (78.4%). These biases were also reflected in research affiliations, which were heavily weighted towards institutions located in Europe and North America, with no first or last authors originating from institutions in Africa or South Asia. There was also substantial variability in the methods used to develop PGSs, with between 3 and 6.6 million variants included in the PGSs. Finally, we observed significant inconsistencies in the reporting of PGS analyses and results, particularly in terms of risk model development and application, coupled with a lack of data transparency and availability, with only three pharmacogenomics PGSs deposited on the Polygenic Score Catalog. These findings highlight current gaps and key areas for future pharmacogenomic PGS research. [ABSTRACT FROM AUTHOR]

The article discusses that the "Clinical and Translational Science" (CTS) journal is looking for research papers in the U.S. It states that the journal continues to welcome high-quality original research submission. It mentions that the papers of the journal will be published in January 2016 as part of the rebranded journal of the American Society for Clinical Pharmacology and Therapeutics (ASCPT).

This article discusses best practices for pharmacokinetic (PK) studies of new chemical entities (NCEs) in drug development. The article highlights the complexities involved in conducting PK studies, including when to conduct them, how to design them, and what analytical methods to use. Two White Papers from international pharmaceutical companies are reviewed, one focusing on intravenous (i.v.) PK and metabolites, and the other on metabolite bioanalysis. The papers provide recommendations for future directions in improving NCE development efficiency. The article emphasizes the importance of these PK studies in aiding decision-making in the early stages of clinical development. [Extracted from the article]

This paper describes the innovative pediatric development strategy of secukinumab in psoriasis. Initially, this pediatric development had been planned early during the adult development program. This is common for most development programs as it is required by law in major regions worldwide. At that time, only limited experience with secukinumab's mechanism of action was available, especially in the pediatric population for which no data had been collected yet. Pediatric extrapolation frameworks were not in use. As a result, a standard pediatric program had initially been proposed requiring two relatively large placebo‐controlled pivotal trials. However, as experience with the use of secukinumab grew, so did experience with—and regulatory openness to—innovative quantitative approaches, such as extrapolation. It became clear that the pediatric development of secukinumab could be substantially accelerated by these novel ideas. Here, we report how these concepts were implemented in the pediatric development program of secukinumab in psoriasis, using a wide range of innovative quantitative techniques. This strategy led to the removal of the placebo arm from one trial, a substantial reduction of the overall sample size in this program, and a significantly faster approval of secukinumab for the pediatric psoriasis population. [ABSTRACT FROM AUTHOR]

In the original publication of the 2020 ASCPT Annual Meeting abstract supplement, the following errors occurred. (1) The authors for several abstracts were listed in an incorrect order due to a transcription error with the online abstract submission system. The author bylines have now been corrected; and (2) The following three abstracts were mis-numbered. [Extracted from the article]

Industry perspective on therapeutic peptide drug-drug interaction assessments during drug development: a European Federation of Pharmaceutical Industries and Associations White Paper. Säll I et al i . report findings from a cross-industry therapeutic peptide DDI working group consisting of drug metabolism and pharmacokinetics experts from 10 leading pharmaceutical and biotechnology companies, established in 2020 under the sponsorship of the European Federation of Pharmaceutical Industries and Associations (EFPIA; www.efpia.eu). Although insulin was discovered more than 100 years ago, the significant shift toward biotherapeutic drug development only began at the start of this century. [Extracted from the article]

As the scientific research community along with healthcare professionals and decision makers around the world fight tirelessly against the coronavirus disease 2019 (COVID‐19) pandemic, the need for comparative effectiveness research (CER) on preventive and therapeutic interventions for COVID‐19 is immense. Randomized controlled trials markedly under‐represent the frail and complex patients seen in routine care, and they do not typically have data on long‐term treatment effects. The increasing availability of electronic health records (EHRs) for clinical research offers the opportunity to generate timely real‐world evidence reflective of routine care for optimal management of COVID‐19. However, there are many potential threats to the validity of CER based on EHR data that are not originally generated for research purposes. To ensure unbiased and robust results, we need high‐quality healthcare databases, rigorous study designs, and proper implementation of appropriate statistical methods. We aimed to describe opportunities and challenges in EHR‐based CER for COVID‐19‐related questions and to introduce best practices in pharmacoepidemiology to minimize potential biases. We structured our discussion into the following topics: (1) study population identification based on exposure status; (2) ascertainment of outcomes; (3) common biases and potential solutions; and (iv) data operational challenges specific to COVID‐19 CER using EHRs. We provide structured guidance for the proper conduct and appraisal of drug and vaccine effectiveness and safety research using EHR data for the pandemic. This paper is endorsed by the International Society for Pharmacoepidemiology (ISPE). [ABSTRACT FROM AUTHOR]

Precision dosing, the tailoring of drug doses to optimize therapeutic benefits and minimize risks in each patient, is essential for drugs with a narrow therapeutic window and severe adverse effects. Adaptive dosing strategies extend the precision dosing concept to time‐varying treatments which require sequential dose adjustments based on evolving patient conditions. Reinforcement learning (RL) naturally fits this paradigm: it perfectly mimics the sequential decision‐making process where clinicians adapt dose administration based on patient response and evolution monitoring. This paper aims to investigate the potentiality of coupling RL with population PK/PD models to develop precision dosing algorithms, reviewing the most relevant works in the field. Case studies in which PK/PD models were integrated within RL algorithms as simulation engine to predict consequences of any dosing action have been considered and discussed. They mainly concern propofol‐induced anesthesia, anticoagulant therapy with warfarin and a variety of anticancer treatments differing for administered agents and/or monitored biomarkers. The resulted picture highlights a certain heterogeneity in terms of precision dosing approaches, applied methodologies, and degree of adherence to the clinical domain. In addition, a tutorial on how a precision dosing problem should be formulated in terms of the key elements composing the RL framework (i.e., system state, agent actions and reward function), and on how PK/PD models could enhance RL approaches is proposed for readers interested in delving in this field. Overall, the integration of PK/PD models into a RL‐framework holds great promise for precision dosing, but further investigations and advancements are still needed to address current limitations and extend the applicability of this methodology to drugs requiring adaptive dosing strategies. [ABSTRACT FROM AUTHOR]

The role of membrane transporters on pharmacokinetics (PKs), drug–drug interactions (DDIs), pharmacodynamics (PDs), and toxicity of drugs has been broadly recognized. However, our knowledge of modulation of transporter expression and/or function in the diseased patient population or specific populations, such as pediatrics or pregnancy, is still emerging. This white paper highlights recent advances in studying the changes in transporter expression and activity in various diseases (i.e., renal and hepatic impairment and cancer) and some specific populations (i.e., pediatrics and pregnancy) with the focus on clinical implications. Proposed alterations in transporter abundance and/or activity in diseased and specific populations are based on (i) quantitative transporter proteomic data and relative abundance in specific populations vs. healthy adults, (ii) clinical PKs, and emerging transporter biomarker and/or pharmacogenomic data, and (iii) physiologically‐based pharmacokinetic modeling and simulation. The potential for altered PK, PD, and toxicity in these populations needs to be considered for drugs and their active metabolites in which transporter‐mediated uptake/efflux is a major contributor to their absorption, distribution, and elimination pathways and/or associated DDI risk. In addition to best practices, this white paper discusses current challenges and knowledge gaps to study and quantitatively predict the effects of modulation in transporter activity in these populations, together with the perspectives from the International Transporter Consortium (ITC) on future directions. [ABSTRACT FROM AUTHOR]

With the globalization of clinical trials, regulators have increased collaboration to evaluate the adequacy of clinical trial conduct and to optimize regulatory oversight. The 2020 joint Good Clinical Practice (GCP) symposium of the US Food and Drug Administration and the UK Medicines and Healthcare products Regulatory Agency provided the agencies' perspectives on the challenges in ensuring data quality in novel clinical trial designs and the importance of the management and documentation of protocol deviations, sponsor oversight of clinical trials, and use of electronic source data, including electronic health records. This paper summarizes considerations of both agencies on these topics, along with case examples. This paper touches upon considerations when using real‐world data to support regulatory decisions. It also discusses the impact of the coronavirus disease 2019 (COVID‐19) pandemic on clinical trial conduct and underscores the importance of well‐designed, resilient, and adaptable systems for GCP compliance and data integrity. [ABSTRACT FROM AUTHOR]

With the promise of a potentially "single dose curative" paradigm, CAR‐T cell therapies have brought a paradigm shift in the treatment and management of hematological malignancies. Both CAR‐T and TCR‐T cell therapies have also made great progress toward the successful treatment of solid tumor indications. The field is rapidly evolving with recent advancements including the clinical development of "off‐the‐shelf" allogeneic CAR‐T therapies that can overcome the long and difficult "vein‐to‐vein" wait time seen with autologous CAR‐T therapies. There are unique clinical pharmacology, pharmacometric, bioanalytical, and immunogenicity considerations and challenges in the development of these CAR‐T and TCR‐T cell therapies. Hence, to help accelerate the development of these life‐saving therapies for the patients with cancer, experts in this field came together under the umbrella of International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) to form a joint working group between the Clinical Pharmacology Leadership Group (CPLG) and the Translational and ADME Sciences Leadership Group (TALG). In this white paper, we present the IQ consortium perspective on the best practices and considerations for clinical pharmacology and pharmacometric aspects toward the optimal development of CAR‐T and TCR‐T cell therapies. [ABSTRACT FROM AUTHOR]

During the coronavirus disease 2019 (COVID‐19) pandemic, the urgency for updated evidence to inform public health and clinical care placed systematic literature reviews (SLRs) at the cornerstone of research. We aimed to summarize evidence on prognostic factors for COVID‐19 outcomes through published SLRs and to critically assess quality elements in the findings' interpretation. An umbrella review was conducted via electronic databases from January 2020 to April 2022. All SLRs (and meta‐analyses) in English were considered. Data screening and extraction were conducted by two independent reviewers. AMSTAR 2 tool was used to assess SLR quality. The study was registered with PROSPERO (CRD4202232576). Out of 4,564 publications, 171 SLRs were included of which 3 were umbrella reviews. Our primary analysis included 35 SLRs published in 2022, which incorporated studies since the beginning of the pandemic. Consistent findings showed that, for adults, older age, obesity, heart disease, diabetes, and cancer were more strongly predictive of risk of hospitalization, intensive care unit admission, and mortality due to COVID‐19. Male sex was associated with higher risk of short‐term adverse outcomes, but female sex was associated with higher risk of long COVID. For children, socioeconomic determinants that may unravel COVID‐19 disparities were rarely reported. This review highlights key prognostic factors of COVID‐19, which can help clinicians and health officers identify high‐risk groups for optimal care. Findings can also help optimize confounding adjustment and patient phenotyping in comparative effectiveness research. A living SLR approach may facilitate dissemination of new findings. This paper is endorsed by the International Society for Pharmacoepidemiology. [ABSTRACT FROM AUTHOR]

Regulators and Health Technology Assessment (HTA) bodies are increasingly familiar with, and publishing guidance on, external controls derived from real‐world data (RWD) to generate real‐world evidence (RWE). We recently conducted a systematic literature review (SLR) evaluating publicly available information on the use of RWD‐derived external controls to contextualize outcomes from uncontrolled trials submitted to the European Medicines Agency (EMA), the US Food and Drug Administration (FDA), and/or select HTA bodies. The review identified several key operational and methodological aspects for which more detailed guidance and alignment within and between regulatory agencies and HTA bodies is necessary. This paper builds on the SLR findings by delineating a set of key takeaways for the responsible generation of fit‐for‐purpose RWE. Practical methodological and operational guidelines for designing, conducting, and reporting RWD‐derived external control studies are explored and discussed. These considerations include: (i) early engagement with regulators and HTA bodies during the study planning phase; (ii) consideration of the appropriateness and comparability of external controls across multiple dimensions, including eligibility criteria, temporality, population representation, and clinical evaluation; (iii) ensuring adequate sample sizes, including hypothesis testing considerations; (iv) implementation of a clear and transparent strategy for assessing and addressing data quality, including data missingness across trials and RWD; (v) selection of comparable and meaningful endpoints that are operationalized and analyzed using appropriate analytic methods; and (vi) conduct of sensitivity analyses to assess the robustness of findings in the context of uncertainty and sources of potential bias. [ABSTRACT FROM AUTHOR]

The most intuitive question for market access for medicinal products is the benefit/risk (B/R) balance. The B/R assessment can conceptually be divided into subquestions related to establishing efficacy and safety. There are additional layers to the B/R ratio for medical products, including questions related to dose selection, clinical and nonclinical pharmacology, and drug quality. Explicitly stating the actual questions and how they contribute to the overall B/R provides a structure that fosters better informed cross‐domain discussions. There is currently no systematic approach in the regulatory setting to assess and establish the acceptability of alternative methods and data sources. In most cases, the medicinal product sponsors tend to prioritize traditional data types and methods, which are well accepted by regulators for inclusion in regulatory submissions. This, in addition to the absence of rigor in the use and validation of new data types and methods, and the limited training of assessors in related fields can lead to increased regulatory skepticism toward new data types and methods. A data‐knowledge backbone is needed to mitigate the uncertainty in efficacy and safety characterization. This white paper discusses the value of explicitly redefining and restructuring the regulatory scientific decision making around the scientific question to be addressed. The ecosystem proposed is based on three pillars: (i) a repository connecting questions, data, and methods; (ii) the development and validation of high‐quality standards for data and methods; and (iii) credibility assessment. The ecosystem is applied to four use cases for illustration. The need for training and regulatory guidance is also discussed. [ABSTRACT FROM AUTHOR]

Although the digital revolution has transformed many areas of human endeavor, pharmaceutical drug development has been relatively slow to embrace the emerging technologies to enhance efficiency and optimize value in clinical trials. The topic has garnered even greater attention in the face of the coronavirus disease 2019 (COVID‐19) outbreak, which has caused unprecedented disruption in the conduct of clinical trials and presented considerable challenges and opportunities for clinical trialists and data analysts. In this paper, we highlight the potential opportunity with virtual or digital clinical trials as viable options to enhance efficiency in drug development and, more importantly, in offering diverse patients easier and attractive means to participate in clinical trials. Special reference is made to the implication of artificial intelligence and machine‐learning tools in trial execution and data acquisition, processing, and analysis in a virtual trial setting. Issues of patient safety, measurement validity, and data integrity are reviewed, and considerations are put forth with reference to the mitigation of underlying regulatory and operational barriers. [ABSTRACT FROM AUTHOR]

Even modest improvements in the probability of success of selecting drug targets which are ultimately approved can substantially reduce the costs of research and development. Drug targets with human genetic evidence of disease association are twice as likely to lead to approved drugs. A key enabler of identifying and validating these genetically validated targets is access to association results from genome‐wide genotyping, whole‐exome sequencing, and whole‐genome sequencing studies with observable traits (often diseases) across large numbers of individuals. Today, linkage between genotype and real‐world data (RWD) provides significant opportunities to not only increase the statistical power of genome‐wide association studies by ascertaining additional cases for diseases of interest, but also to improve diversity and coverage of association studies across the disease phenome. As RWD‐genetics linked resources continue to grow in diversity of participants, breadth of data captured, length of observation, and number of participants, there is a greater need to leverage the experience of RWD experts, clinicians, and highly experienced geneticists together to understand which lessons and frameworks from general research using RWD sources are relevant to improve genetics‐driven drug discovery and development. This paper describes new challenges and opportunities for phenotypes enabled by diverse RWD sources, considerations in the use of RWD phenotypes for disease gene identification across the disease phenome, and challenges and opportunities in leveraging RWD phenotypes in target validation. The paper concludes with views on the future directions for phenotype development using RWD, and key questions requiring further research and development to advance this nascent field. [ABSTRACT FROM AUTHOR]