Use of Real-World Evidence to Drive Drug Development Strategy and Inform Clinical Trial Design

Exploring immune checkpoint inhibitors: Focus on PD-1/PD-L1 axis and beyond

Immunotherapy emerges as a promising approach, marked by recent substantial progress in elucidating how the host immune response impacts tumor development and its sensitivity to various treatments. Immune checkpoint inhibitors have revolutionized cancer therapy by unleashing the power of the immune system to recognize and eradicate tumor cells. Among these, inhibitors targeting the programmed cell death protein 1 (PD-1) and its ligand (PD-L1) have garnered significant attention due to their remarkable clinical efficacy across various malignancies. This review delves into the mechanisms of action, clinical applications, and emerging therapeutic strategies surrounding PD-1/PD-L1 blockade. We explore the intricate interactions between PD-1/PD-L1 and other immune checkpoints, shedding light on combinatorial approaches to enhance treatment outcomes and overcome resistance mechanisms. Furthermore, we discuss the expanding landscape of immune checkpoint inhibitors beyond PD-1/PD-L1, including novel targets such as CTLA-4, LAG-3, TIM-3, and TIGIT. Through a comprehensive analysis of preclinical and clinical studies, we highlight the promise and challenges of immune checkpoint blockade in cancer immunotherapy, paving the way for future advancements in the field.

Optimizing Treatment: The Role of Pharmacology, Genomics, and AI in Improving Patient Outcomes

Recent advances in pharmacology are revolutionizing drug discovery and treatment strategies through personalized medicine, pharmacogenomics, and artificial intelligence (AI). The objective of the present study is to review the role of personalized medicine, pharmacogenomics, and AI-based strategies in optimizing patient outcomes with improved drug efficacy and reduced side effects. A comprehensive review was performed to debate the utility of pharmacogenomics in the prediction of drug response, the role of AI in drug discovery, and the utility of personalized medicine in the clinic. This review highlights how drug discovery and treatment techniques are evolving with the aid of personalized medicine, pharmacogenomics, and AI. Personalized medicine makes the treatment fit the DNA pattern for higher efficacy and minimal side effects. Pharmacogenomics forecasts the action of a drug in terms of genetic difference. AI speeds up drug discovery to enhance the effectiveness and accuracy of finding and evaluating drug leads. Studies show that customized medicine charts therapy to an individual patient's individual genetic profile, resulting in better therapy. Pharmacogenomics facilitates precise drug selection by considering genetic variations, reducing adverse reactions. AI speeds up drug discovery by applying predictive modeling and data-driven evaluation to propel optimized drug development pathways. Together, these advances are enabling more efficient and safer treatment practices across medical disciplines. The combination of pharmacology, genomics, and AI is revolutionizing contemporary healthcare through the personalization of treatments, improved drug safety, and therapeutic outcomes. The future of research should be on optimizing these techniques and overcoming ethical and regulatory issues to facilitate broader clinical implementation.

Combination Therapies in Drug Repurposing: Personalized Approaches to Combatting Leukaemia and Multiple Myeloma

Despite advances in cancer research, treating malignancies remains challenging due to issues like drug resistance, disease heterogeneity, and the limited efficacy of current therapies, particularly in relapsed or refractory cases. In recent years, several drugs originally approved for non-cancer indications have shown potential in cancer treatment, demonstrating anti-proliferative, anti-metastatic, and immunomodulatory effects. Drug repurposing has shown immense promise due to well-established safety profiles and mechanisms of action of the compounds. However, the implementation is fraught with clinical, logistical, regulatory, and ethical challenges, especially in diseases such as leukaemia and multiple myeloma. This chapter examines the treatment challenges in leukaemia and multiple myeloma, focusing on the role of drug repurposing in addressing therapeutic resistance and disease variability. It highlights the potential of personalized, tailored combination therapies, using repurposed drug components, to offer more effective, targeted, and cost-efficient treatment strategies, overcoming resistance and improving patient outcomes.

Patient data for commercial companies? An ethical framework for sharing patients' data with for-profit companies for research

BACKGROUND

Research using data from medical care promises to advance medical science and improve healthcare. Academia is not the only sector that expects such research to be of great benefit. The research-based health industry is also interested in so-called 'real-world' health data to develop new drugs, medical technologies or data-based health applications. While access to medical data is handled very differently in different countries, and some empirical data suggest people are uncomfortable with the idea of companies accessing health information, this paper aims to advance the ethical debate about secondary use of medical data generated in the public healthcare sector by for-profit companies for medical research (ReuseForPro).

METHODS

We first clarify some basic concepts and our ethical-normative approach, then discuss and ethically evaluate potential claims and interests of relevant stakeholders: patients as data subjects in the public healthcare system, for-profit companies, the public, and physicians and their healthcare institutions. Finally, we address the tensions between legitimate claims of different stakeholders in order to suggest conditions that might ensure ethically sound ReuseForPro.

RESULTS

We conclude that there are good reasons to grant for-profit companies access to medical data if they meet certain conditions: among others they need to respect patients' informational rights and their actions need to be compatible with the public's interest in health benefit from ReuseForPro.

Co-Encapsulation of Multiple Antineoplastic Agents in Liposomes by Exploring Microfluidics

The inherent complexity of cancer proliferation and malignancy cannot be addressed by the conventional approach of relying on high doses of a single powerful anticancer agent, which is associated with poor efficacy, higher toxicity, and the development of drug resistance. Multiple drug therapy (MDT) rationally designed to target tumor heterogeneity, block alternative survival pathways, modulate the tumor microenvironment, and reduce toxicities would be a viable solution against cancer. Liposomes are the most suitable carrier for anticancer MDT due to their ability to encapsulate both hydrophilic and hydrophobic agents, biocompatibility, and controlled release properties; however, an adequate manufacturing method is important for effective co-encapsulation. Microfluidics involves the manipulation of fluids at the microscale for the controlled synthesis of liposomes with desirable properties. This work critically reviews the use of microfluidics for the synthesis of anticancer MDT liposomes. MDT success not only relies on the identification of synergistic dose combinations of the anticancer modalities but also warrants the loading of multiple therapeutic entities within liposomes in optimal ratios, the protection of the drugs by the nanocarrier during systemic circulation, and the synchronous release at the target site in the same pattern as confirmed in preliminary efficacy studies. Prospects have been identified for the bench-to-bedside translation of anticancer MDT liposomes using microfluidics.

Importance of Health Economics and Outcomes Research in the Product Lifecycle

Health economics and outcomes research (HEOR) has become an integral part of healthcare systems, through its ability to authentically demonstrate the value of the product. HEOR provides healthcare stakeholders with important insights to make informed decisions regarding healthcare delivery. This review aims to highlight the pivotal role of HEOR across the product lifecycle and the value of integrating HEOR activities during the various phases of drug development. Pharmaceutical companies are increasingly realizing that the integration of HEOR activities from early phases of product development through product launch, also during the postmarketing phase, to generate real-world evidence (RWE) can be crucial for their product's continued commercial success. HEOR helps validate the value of a pharmaceutical product, enabling its success in distinct regulatory and health technology assessment (HTA) landscapes across varied geographies. Regardless of several challenges in data collection and analysis, technological advancements facilitate opportunities to improve the value of HEOR. With rising demands for robust clinical evidence by global regulators and economic evidence by HTA agencies and payers, HEOR will become even more crucial in establishing long-lasting value of a pharmaceutical product for all stakeholders, including regulators, patients, prescribers, and payers.

Turning the kaleidoscope: Innovations shaping the future of clinical trial design

Current clinical trials are based on rigid designs and drug-centric approaches that can stifle flexibility and innovation. With advances in molecular biology and technology, there is an urgent call to revitalize trial designs to meet these evolving demands. We propose a reshaped, prismatic vision of clinical trials combining different knowledge layers, synergized with modern computational approaches. This paradigm based on iterative learning will enable a more adaptive and precise framework for oncology drug development.

Unlocking the Mysteries of Rare Disease Drug Development: A Beginner's Guide for Clinical Pharmacologists

Clinical pharmacologists face unique challenges when developing drugs for rare diseases. These conditions are characterized by small patient populations, diverse disease progression patterns, and a limited understanding of underlying pathophysiology. This tutorial serves as a comprehensive guide, offering practical insights and strategies to navigate its complexities. In this tutorial, we outline global regulatory incentives and resources available to support rare disease research, describe some considerations for designing a clinical development plan for rare diseases, and we highlight the role of biomarkers, real-world data, and modeling and simulations to navigate rare disease challenges. By leveraging these tools and understanding regulatory pathways, clinical pharmacologists can significantly contribute to advancing therapeutic options for rare diseases.

Use of Real-World Data and Real-World Evidence in Rare Disease Drug Development: A Statistical Perspective

Real-world data (RWD) and real-world evidence (RWE) have been increasingly used in medical product development and regulatory decision-making, especially for rare diseases. After outlining the challenges and possible strategies to address the challenges in rare disease drug development (see the accompanying paper), the Real-World Evidence (RWE) Scientific Working Group of the American Statistical Association Biopharmaceutical Section reviews the roles of RWD and RWE in clinical trials for drugs treating rare diseases. This paper summarizes relevant guidance documents and frameworks by selected regulatory agencies and the current practice on the use of RWD and RWE in natural history studies and the design, conduct, and analysis of rare disease clinical trials. A targeted learning roadmap for rare disease trials is described, followed by case studies on the use of RWD and RWE to support a natural history study and marketing applications in various settings.

Demographic clinical trial diversity assessment methods: Use of real-world data

Diversity in clinical trials is defined by the inclusion of clinical trial participants from various demographic groups that are representative of the broader population impacted by a disease state. Diversity in clinical trials is critical in identifying potential differences in safety and efficacy of treatments across races, ethnicities, ages, sexes, or other variables. In the United States, clinical trial diversity is often benchmarked against US Census data, which may limit the representativeness of patient demographics in clinical trials. Disease-specific, demographic estimates from real-world data (RWD) can facilitate benchmarking of clinical trials, support trial enrollment and the development of trial diversity plans. Notably, development and dissemination of these estimates from RWD can be challenging without a standardized process. To address this issue, we developed a new evaluation framework to assess patient demographics and characteristics within specific disease populations using RWD and disease population estimates. Suitable databases were identified using predefined criteria such as accessibility to patient-level data, availability of all demographic variables of interest, sufficient sample size of the disease population, and availability of population weights to enhance generalizability. Concurrent data were gathered via targeted literature reviews for each disease condition. Together, this data was used to create disease-specific, demographic estimate profiles to inform diverse enrollment goals for prospective clinical trials. We present two examples of application of this framework to illustrate the results in the case of two disease states, rheumatoid arthritis and stroke.

Decision-Making Criteria and Methods for Initiating Late-Stage Clinical Trials in Drug Development From a Multi-Stakeholder Perspective: A Scoping Review

The decision-making process in drug development involves "go/no-go" decisions, particularly at the transition from early to late-stage trials. While the decisions are solely made by drug developers, they must take into account the perspectives of multiple stakeholders-such as regulatory agencies, HTA bodies, payers, patients, and ethics committees-to ensure well-informed and robust decision-making. These perspectives influence key considerations, including resource allocation, risk mitigation, regulatory compliance, etc. To support this process, quantitative methodologies, including Bayesian and hybrid frequentist-Bayesian approaches, have been introduced to improve decision-making. However, these methodologies often do not fully account for the diverse priorities and needs of all stakeholders. This scoping review examines criteria and methods used in decision-making at the phase II to III transition, with a focus on broadening the probability of success (PoS) concept beyond efficacy alone. Our review explores PoS for different success definitions, such as regulatory approval, market access, financial viability, and competitive performance. Key themes include decision criteria selection, trial design optimization, utility-based approaches, financial metrics, and multi-stakeholder considerations in decision-making. Our findings highlight both the limitations of current methodologies and potential paths forward, including the integration of real-world data (RWD) and advanced analytics. This work complements a companion manuscript by Cetinyurek-Yavuz et al. (2025) providing a detailed review of PoS methodologies focused solely on efficacy, specifically PoS for achieving statistical significance in phase III studies, including definitions, terminologies, and analytical approaches. Together, these studies provide a foundation for advancing late-stage trial decisions toward a more balanced, data-driven, and stakeholder-aligned approach.

Clinical signatures of SYNGAP1-related disorders through data integration

PURPOSE

SYNGAP1 is a genetic neurodevelopmental disorder characterized by generalized epilepsy, autism, and intellectual disability. Despite a comparatively high prevalence, the longitudinal landscape remains relatively unexplored, and complete characterization is essential for clinical trial readiness.

METHODS

We combined electronic medical record data (n = 158) with insurance claims data (n = 246) to evaluate longitudinal progression of symptoms.

RESULTS

Phenotypes associated with SYNGAP1 included behavioral abnormalities (odds ratio [OR]: 12.35, 95% CI: 9.21-16.78), generalized-onset seizures (OR: 1.56, 95% CI: 1.20-2.02), autism (OR: 12.23, 95% CI: 9.29-16.24), and a developmental profile with prominent deficits in verbal skill acquisition. Several clinical features showed distinct age-related patterns, such as a more than 5-fold risk of autistic behavior emerging between 27 and 30 months. Generalized-onset seizures were significantly increased (OR: 4.05, 95% CI: 2.02-7.59) after 3 years of age and persisted over time. Valproic acid and clobazam were commonly used for epilepsy treatment, whereas risperidone, aripiprazole, and guanfacine were commonly used for behavior management. Valproate and lamotrigine were more effective at reducing seizure frequencies or maintaining seizure freedom than other antiseizure medications.

CONCLUSION

We delineated the seizure, developmental, and behavioral trajectories in SYNGAP1-related disorders, to improve diagnosis, prognosis, and clinical care, and facilitating clinical trial readiness.

Real-World Tolerability of Capecitabine and Oxaliplatin in Patients in the United States With Localized Colorectal Cancer Undergoing Curative-Intent Treatment

PURPOSE

The combination of capecitabine and oxaliplatin (CAPOX) is commonly used in patients with localized colorectal cancer (CRC) receiving curative-intent treatment. Our study aimed to assess the real-world tolerability of CAPOX in a single-institution cohort of patients with localized CRC.

METHODS

This is a single-institution retrospective study that included patients with localized CRC receiving neoadjuvant or adjuvant CAPOX. The primary end point was completion rate of intended number (obtained by chart review) of CAPOX cycles irrespective of dose levels. Secondary outcome measures included the rate of grade ≥3 adverse events, hospital admission rate, and dose reductions.

RESULTS

The study included 153 patients with a median age of 61 years; 49% were female and 78.4% had stage III CRC. The proportion of patients (95% CI) who completed all planned CAPOX cycles was 44.4% (36 to 52) in the entire cohort and 34.6% (23 to 45) among female patients. Independent variables associated with treatment completion in multivariable analysis were race, sex, and intended number of cycles. Notably, the therapy completion rates (95% CI) were 55% (43 to 66) and 33% (20 to 45) in patients intended to receive four and eight cycles of CAPOX, respectively. The rate of grade ≥3 adverse events and hospitalization because of CAPOX-related toxicity were 30.7% (95% CI, 23 to 38) and 17.6% (95% CI, 11 to 23), respectively.

CONCLUSION

This study highlights that a substantial number of patients with localized CRC undergoing curative-intent treatment with CAPOX do not complete the planned cycles of chemotherapy because of toxicity. These findings underscore the need for careful patient selection and appropriate supportive care to optimize the therapeutic benefit of CAPOX in this setting.

Patient and public involvement and engagement in real world data and evidence research across the medicines development cycle: a rapid review of peer-reviewed literature and NICE technology appraisals

OBJECTIVES

The objective of this rapid review is to understand the reporting, role and quality of patient and public involvement and engagement (PPIE) in real world data and evidence (RWDE) research across the medicines development cycle.

METHODS

We comprehensively searched, with no date restrictions, Medline and Embase databases for peer-reviewed literature and conference abstracts (Embase only) reporting PPIE in RWD studies. We also assessed PPIE in a sample of 100 NICE technology appraisals (TAs) comprising both single technology appraisals (STAs) and highly specialized technologies (HSTs). We used standard methods for screening and data extraction. In addition, we used the Patient Focused Medicines Development (PFMD)'s Patient Engagement Quality Guidance (PEQG) as a framework to assess the quality of PPIE. We planned to conduct narrative synthesis of included studies, however there were insufficient studies and data reported.

RESULTS

We included three RWD studies that reported PPIE from the peer-reviewed literature and two NICE HSTs. One of the HSTs included data from one of the peer-reviewed journal articles. Reporting of PPIE in included studies was limited. No studies reported a PPIE framework and it was unclear how integrated and meaningful PPIE was. Four out of seven of PFMD's quality criteria for PPIE were poorly reported by included studies. This suggests reporting and/or conduct of PPIE requires improvement in RWD studies.

CONCLUSIONS

Our review found that PPIE was rarely reported in RWDE research and uncovers a need for consistent reporting. For most publications there was insufficient information to judge the extent to which patients and carers, were considered meaningful partners. However, our review provided preliminary evidence that PPIE can influence protocol development, recruitment, and retention methods in RWD studies. More inclusive approaches to PPIE would help interpretation of RWE regarding relevance and importance to patients and carers.

The relevance of the real-world evidence in research, clinical, and regulatory decision making

The scientific method has been established as the optimal approach for systematically gathering and interpreting data on various human phenomena, mainly through the adoption of strict experimental methods, such as controlled randomized trials, which is relevant for clinical decision-making and research, but also weights the regulatory processes for approval of drugs and other medical products. However, a key factor in strict methods is the generalizability of findings that may be limited to specific settings and patient characteristics. This limitation can be addressed by non-experimental methods aimed at investigating populations in naturalistic routine clinical settings, which may offer a more representative reflection of the usage, effectiveness, and safety of healthcare interventions. These approaches can generate the so-called real-world data and the resulting real-world evidence. In this narrative review, we present these concepts, explore the potential applications and advantages of real-world evidence for clinical, research, and regulatory decision-making, and discuss the challenges of employing it, the solutions to improve its generation, and lastly the current level of evidence required for the integration of real-world evidence into regulatory decision-making. We concluded that the advantages of using it, when utilized in a balanced manner, overcome the challenges and, therefore, can offer a time- and cost-saving solution for researchers, the healthcare industry, regulatory agencies, policymakers, and payers towards the patient benefit. The knowledge generated by this approach provides valuable additional insights into medical interventions in real patients under realistic daily scenarios.

Development of cohort definitions and algorithms to identify patients with Lennox-Gastaut syndrome or Dravet syndrome from real-world administrative healthcare databases

Objective

To develop cohort definitions and algorithms that can be applied to a range of real-world retrospective data sources in the United States, France, Germany, Italy, Spain, United Kingdom, China, and Japan to identify patients with Lennox-Gastaut syndrome (LGS) or Dravet syndrome (DS) to aid future research.

Methods

The study was conducted in 3 stages. A targeted literature review was used to identify retrospective healthcare database studies identifying LGS or DS populations and develop overall draft cohort definitions and algorithms. Country-specific research explored the diagnosis codes and antiseizure medications (ASMs) with available indications for LGS or DS, with the findings used to adapt the cohort definitions and algorithms to country-specific settings. Physician interviews were conducted to validate and refine the draft country-specific cohort definitions and algorithms, and better understand the diagnosis and treatment of patients with LGS and DS in each country.

Results

Forty-eight publications were reviewed; 25 focused on LGS only and 14 on DS only. LGS-specific and DS-specific diagnosis codes were identified in the United States, Spain, and United Kingdom; local codes in France, China, and Japan; and no LGS-specific or DS-specific diagnosis codes in Germany or Italy. ASMs only indicated for LGS or DS were available across all countries except China. Multiple seizure types, developmental delay, and intellectual disabilities were considered by the physicians to be key diagnostic features. The country-specific definitions for all 3 approaches (specific diagnostic codes, ASM indicated for LGS or DS only, and broader epilepsy diagnostic codes) were refined further using consensus from the physician interviews.

Conclusions

To our knowledge, this is the first study to comprehensively develop country-specific definitions and algorithms for LGS and DS across several countries that provide a solid foundation toward identifying such patients from real-world retrospective databases. However, additional research and validation using real-world data sources are warranted.

Integrating machine learning and human use experience to identify personalized pharmacotherapy in Traditional Chinese Medicine: a case study on resistant hypertension

OBJECTIVE

To enhance the understanding of identifying personalized pharmacotherapy options in Traditional Chinese Medicine (TCM), and further support the registration of new TCM drugs.

METHODS

Generalized Boosted Models and XGBoost were employed to construct a classification model to identify the bad prognosis factors in resistant hypertension (RH) patients. Furthermore, we used association analysis to explore the rules of "symptom-syndrome" and "symptom-herb" for the major influencing factors, in order to summarize prescription pattern and applicable patients of TCM.

RESULTS

Patients with major adverse cardiac events mostly have complex symptoms of phlegm, stasis, deficiency and fire intermingled with each other, and finally summarized the human experience of using Chinese herbal medicine to precisely intervene in some symptoms of RH patients on the basis of conventional Western medical treatment.

CONCLUSIONS

Machine learning algorithms can make full use of human use experience and evidence to save clinical trial resources and accelerate the development of TCM varieties.

Augmenting clinical trials in asthma through digital technology, decentralised designs, and person-centric endpoints: opportunities and challenges

Digital technologies (eg, smart inhalers, wearables, and sensors) allow for remote, objective, granular, and non-invasive data collection, making them attractive for research evaluating interventions in airways diseases with variable trajectories, such as asthma. Such technologies offer the opportunity to move towards decentralised clinical trials that are done partly or fully outside the classic clinical trial setting and are characterised by remote data collection and monitoring. This approach to evaluating clinical, pharmacological, or behavioural interventions could facilitate recruitment of inclusive and generalisable study populations, enhance personalisation and sustainability, reduce research costs, and accelerate the timeline to novel asthma treatments' market access. This Personal View discusses the application of digital technologies and endpoints within trials; the concept of hybrid and decentralised designs; describes a fully decentralised trial in asthma; and explores the strengths, weaknesses, opportunities, and threats regarding their implementation from the clinician, patient expert, low-resource, and regulator viewpoints.

Cancer Therapy-Induced Cardiotoxicity: Results of the Analysis of the UK DEFINE Database

BACKGROUND

The accelerated development of novel cancer therapies necessitates a thorough understanding of the associated cardiotoxicity profiles, due to their significant implications for the long-term health and quality of life of cancer survivors.

OBJECTIVES

The aim of this study was to determine the association between cardiotoxicity and non-small cell lung cancer (NSCLC) treatments using a hospital medicines usage database in England.

METHODS

An observational study based on a retrospective design using real-world data from the UK DEFINE database was performed. Monthly secondary data of 40 shortlisted drugs from April 2017 to July 2022 were extracted.

RESULTS

The cardiology drug that was associated with most oncology drugs was apixaban. Atezolizumab, bevacizumab, nintedanib, osimertinib, paclitaxel, pembrolizumab, gemcitabine and vincristine were all mostly associated with apixaban, which indicated association with atrial fibrillation. Afatinib, erlotinib and methotrexate were mostly associated with atenolol, hence suggesting the association with ischaemia or hypertension. Docetaxel and epirubicin were associated with verapamil, which indicated association with arrhythmia or hypertension.

CONCLUSIONS

From the correlation and regression analyses, it can be concluded that hypertension was the most associated cardiovascular disease with the 20 shortlisted oncology drugs. The findings of this study have provided a better understanding of the association between each NSCLC-Cardio drug pair.

Single-arm clinical trials: design, ethics, principles

Although randomised controlled trials are considered the gold standard in clinical research, they are not always feasible due to limitations in the study population, challenges in obtaining evidence, high costs and ethical considerations. As a result, single-arm trial designs have emerged as one of the methods to address these issues. Single-arm trials are commonly applied to study advanced-stage cancer, rare diseases, emerging infectious diseases, new treatment methods and medical devices. Single-arm trials have certain ethical advantages over randomised controlled trials, such as providing equitable treatment, respecting patient preferences, addressing rare diseases and timely management of adverse events. While single-arm trials do not adhere to the principles of randomisation and blinding in terms of scientific rigour, they still incorporate principles of control, balance and replication, making the design scientifically reasonable. Compared with randomised controlled trials, single-arm trials require fewer sample sizes and have shorter trial durations, which can help save costs. Compared with cohort studies, single-arm trials involve intervention measures and reduce external interference, resulting in higher levels of evidence. However, single-arm trials also have limitations. Without a parallel control group, there may be biases in interpreting the results. In addition, single-arm trials cannot meet the requirements of randomisation and blinding, thereby limiting their evidence capacity compared with randomised controlled trials. Therefore, researchers consider using single-arm trials as a trial design method only when randomised controlled trials are not feasible.

Health equity innovation in precision medicine: data stewardship and agency to expand representation in clinicogenomics

Most forms of clinical research examine a very minute cross section of the patient journey. Much of the knowledge and evidence base driving current genomic medicine practice entails blind spots arising from underrepresentation and lack of research participation in clinicogenomic databases. The flaws are perpetuated in AI models and clinical practice guidelines that reflect the lack of diversity in data being used. Participation in clinical research and biobanks is impeded in many populations due to a variety of factors that include knowledge, trust, healthcare access, administrative barriers, and technology gaps. A recent symposium brought industry, clinical, and research participants in clinicogenomics to discuss practical challenges and potential for new data sharing models that are patient centric and federated in nature and can address health disparities that might be perpetuated by lack of diversity in clinicogenomic research, biobanks, and datasets. Clinical data governance was recognized as a multiagent problem, and governance practices need to be more patient centric to address most barriers. Digital tools that preserve privacy, document provenance, and enable the management of data as intellectual property have great promise. Policy updates realigning and rationalizing clinical data governance practices are warranted.

The next frontier in multiple sclerosis therapies: Current advances and evolving targets

Recent advancements in research have significantly enhanced our comprehension of the intricate immune components that contribute to multiple sclerosis (MS) pathogenesis. By conducting an in-depth analysis of complex molecular interactions involved in the immunological cascade of the disease, researchers have successfully identified novel therapeutic targets, leading to the development of innovative therapies. Leveraging pioneering technologies in proteomics, genomics, and the assessment of environmental factors has expedited our understanding of the vulnerability and impact of these factors on the progression of MS. Furthermore, these advances have facilitated the detection of significant biomarkers for evaluating disease activity. By integrating these findings, researchers can design novel molecules to identify new targets, paving the way for improved treatments and enhanced patient care. Our review presents recent discoveries regarding the pathogenesis of MS, highlights their genetic implications, and proposes an insightful approach for engaging with newer therapeutic targets in effectively managing this debilitating condition.

Analysis of factors associated with use of real-world data in single technology appraisals of cancer drugs by the National Institute for Health and Care Excellence

OBJECTIVES

This study investigates factors associated with use of real-world data (RWD) in economic modelling for single technology appraisals (STAs) of cancer drugs by the National Institute for Health and Care Excellence (NICE) to improve systematic understanding of the use of RWD.

METHODS

The data were extracted from STAs of cancer drugs, for which NICE issued guidance between January 2011 and December 2022 (n=267). Binary regression was used to test hypotheses concerning the greater or lesser use of RWD. Bonferroni-Holm correction was used to control error rates in multiple hypotheses tests. Several explanatory variables were considered in this analysis, including time (Time), incidence rate of disease (IR), availability of direct treatment comparison (AD), generalisability of trial data (GE), maturity of survival data in trial (MS) and previous technology recommendations by NICE (PR). The primary outcome variable was any use of RWD. Secondary outcome variables were specific uses of RWD in economic models.

RESULTS

AD had a statistical negative association with any use of RWD whereas no associations with non-parametric and parametric use of RWD were found. Time had several statistical associations with use of RWD (validating survival distributions for the intervention, estimating progression-free survival for the intervention, estimating overall survival for comparators and transition probabilities).

CONCLUSIONS

RWD were more likely to be used in economic modelling of cancer drugs when randomised controlled trials failed to provide relevant clinical information of the drug for appraisals, particularly in the absence of direct treatment comparisons. These results, based on analysis of data systematically collected from previous appraisals, suggest that uses of RWD were associated with data gaps in the economic modelling. While this result may support some of the claimed advantages of using RWD when evidence is absent, the question, the extent to which use of RWD in indirect treatment comparisons reduces uncertainty is still to be determined.

Adverse Reactions to the Orphan Drug Cerliponase Alfa in the Treatment of Neurolipofuscinosis Type 2 (CLN2)

Background/Objectives: Neuronal Ceroid Lipofuscinosis type 2 is a rare pathology affecting mainly the central nervous system (CNS) and retina, and is caused by variants in the gene encoding the lysosomal enzyme tripeptidyl peptidase 1. Therapy with enzyme replacement through the brain infusion of the orphan drug cerliponase alfa, a recombinant human tripeptidyl peptidase 1 enzyme replacement therapy delivered via intracerebroventricular infusion, has been approved for Neuronal Ceroid Lipofuscinosis type 2 disease. The safety profile of cerliponase alfa has been established based on pre-authorization studies; currently, no post-marketing investigation has been performed to confirm it. Here, a descriptive analysis of real-world spontaneous reporting data of suspected adverse reactions (SARs) to cerliponase alfa in the EudraVigilance database was performed to compile clear information on the safety profile. Methods: Suspected adverse reactions to cerliponase alfa reported in the data system EudraVigilance were analyzed for age, sex of the patient, adverse reactions, and the indication for use. Results: Cases with suspected adverse reactions to cerliponase alfa were found to be more frequent in female patients (58.1%) and in children aged 3-11 years. The most common adverse reactions were, in decreasing order, fever/pyrexia, device-related infection, vomiting, seizures/convulsions, pleocytosis, irritability, ventriculitis, and respiratory disorders. Conclusions: The results confirm the safety profile of cerliponase alfa established with pre-registration clinical studies but suggest the need for further studies to investigate the occurrence of adverse reactions, as possible predictive prognostic markers, in more depth.

Advancements in Virtual Bioequivalence: A Systematic Review of Computational Methods and Regulatory Perspectives in the Pharmaceutical Industry

BACKGROUND/OBJECTIVES

The rise of virtual bioequivalence studies has transformed the pharmaceutical landscape, enabling more efficient drug development processes. This systematic review aims to explore advancements in physiologically based pharmacokinetic (PBPK) modeling, its regulatory implications, and its role in achieving virtual bioequivalence, particularly for complex drug formulations.

METHODS

We conducted a systematic review of clinical trials using computational methods, particularly PBPK modeling, to carry out bioequivalence assessments. Eligibility criteria are emphasized during in silico modeling and pharmacokinetic simulations. Comprehensive literature searches were performed across databases such as PubMed, Scopus, and the Cochrane Library. A search strategy using key terms and Boolean operators ensured that extensive coverage was achieved. We adhered to the PRISMA guidelines in regard to the study selection, data extraction, and quality assessment, focusing on key characteristics, methodologies, outcomes, and regulatory perspectives from the FDA and EMA.

RESULTS

Our findings indicate that PBPK modeling significantly enhances the prediction of pharmacokinetic profiles, optimizing dosing regimens, while minimizing the need for extensive clinical trials. Regulatory agencies have recognized this utility, with the FDA and EMA developing frameworks to integrate in silico methods into drug evaluations. However, challenges such as study heterogeneity and publication bias may limit the generalizability of the results.

CONCLUSIONS

This review highlights the critical need for standardized protocols and robust regulatory guidelines to facilitate the integration of virtual bioequivalence methodologies into pharmaceutical practices. By embracing these advancements, the pharmaceutical industry can improve drug development efficiency and patient outcomes, paving the way for innovative therapeutic solutions. Continued research and adaptive regulatory frameworks will be essential in navigating this evolving field.

A Comprehensive Framework for Evaluating the Value Created by Real-World Evidence for Diverse Stakeholders: The Case for Coordinated Registry Networks

OBJECTIVES

This manuscript presents a comprehensive framework for the assessment of the value of real-world evidence (RWE) in healthcare decision-making. While RWE has been proposed to overcome some limitations of traditional, one-off studies, no systematic framework exists to measure if RWE actually lowers the burden. This framework aims to fill that gap by providing conceptual approaches for evaluating the time and cost efficiencies of RWE, thus guiding strategic investments in RWE infrastructure.

METHODS

The framework consists of four components: (114th Congress. 21st Century Cures Act.; 2015. https://www.congress.gov/114/plaws/publ255/PLAW-114publ255.pdf .) identification of stakeholders using and producing RWE, (National Health Council. Glossary of Patient Engagement Terms. Published 2019. Accessed May 18. 2021. https://nationalhealthcouncil.org/glossary-of-patient-engagement-terms/ .) understanding value propositions on how RWE can benefit stakeholders, (Center for Drug Evaluation and Research. CDER Patient-Focused Drug Development. U.S. Food & Drug Administration.) defining key performance indicators (KPIs), and (U.S. Department of Health and Human Services - Food and Drug Administration: Center for Devices and Radiological Health and Center for Biologics Evaluation and Research. Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices - Guidance for Industry and Food and Drug Administration Staff. 2017. http://www.fda.gov/BiologicsBloodVaccines/GuidanceComplianceRegulatoryInformation/Guida .) establishing metrics and case studies to assess value. KPIs are categorized as 'better, faster, or cheaper" as an indicator of value: better focusing on high-quality actionable evidence; 'faster,' denoting time-saving in evidence generation, and 'cheaper,' emphasizing cost-efficiency decision compared to methodologies that do not involve data routinely collected in clinical practice. Metrics and relevant case studies are tailored based on stakeholder value propositions and selected KPIs that can be used to assess what value has been created by using RWE compared to traditional evidence-generation approaches and comparing different RWE sources.

RESULTS

Operationalized through metrics and case studies drawn from the literature, the value of RWE is documented as improving treatment effect heterogeneity evaluation, expanding medical product labels, and expediting post-market compliance. RWE is also shown to reduce the cost and time required to produce evidence compared to traditional one-off approaches. An original example of a metric that measures the time saved by RWE methods to detect a signal of a product failure was presented based on analysis of the National Cardiovascular Disease Registry.

CONCLUSIONS

The framework presented in this manuscript offers a comprehensive approach for evaluating the value of RWE, applicable to all stakeholders engaged in leveraging RWE for healthcare decision-making. Through the proposed metrics and illustrated case studies, valuable insights are provided into the heightened efficiency, cost-effectiveness, and improved decision-making within clinical and regulatory domains facilitated by RWE. While this framework is primarily focused on medical devices, it could potentially inform the determination of RWE value in other medical products. By discerning the variations in cost, time, and data utility among various evidence-generation methods, stakeholders are empowered to invest strategically in RWE infrastructure and shape future research endeavors.

Realizing the potential of social determinants data in EHR systems: A scoping review of approaches for screening, linkage, extraction, analysis, and interventions

Background

Social determinants of health (SDoH), such as socioeconomics and neighborhoods, strongly influence health outcomes. However, the current state of standardized SDoH data in electronic health records (EHRs) is lacking, a significant barrier to research and care quality.

Methods

We conducted a PubMed search using "SDOH" and "EHR" Medical Subject Headings terms, analyzing included articles across five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.

Results

Of 685 articles identified, 324 underwent full review. Key findings include implementation of tailored screening instruments, census and claims data linkage for contextual SDoH profiles, NLP systems extracting SDoH from notes, associations between SDoH and healthcare utilization and chronic disease control, and integrated care management programs. However, variability across data sources, tools, and outcomes underscores the need for standardization.

Discussion

Despite progress in identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical for SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately, widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

Informatics and Artificial Intelligence-Guided Assessment of the Regulatory and Translational Research Landscape of First-in-Class Oncology Drugs in the United States, 2018-2022

PURPOSE

Cancer drug development remains a critical but challenging process that affects millions of patients and their families. Using biomedical informatics and artificial intelligence (AI) approaches, we assessed the regulatory and translational research landscape defining successful first-in-class drugs for patients with cancer.

METHODS

This is a retrospective observational study of all novel first-in-class drugs approved by the US Food and Drug Administration (FDA) from 2018 to 2022, stratified by cancer versus noncancer drugs. A biomedical informatics pipeline leveraging interoperability standards and ChatGPT performed integration and analysis of public databases provided by the FDA, National Institutes of Health, and WHO.

RESULTS

Between 2018 and 2022, the FDA approved a total of 247 novel drugs, of which 107 (43.3%) were first-in-class drugs involving a new biologic target. Of these first-in-class drugs, 30 (28%) treatments were indicated for patients with cancer, including 19 (63.3%) for solid tumors and the remaining 11 (36.7%) for hematologic cancers. A median of 68 publications of basic, clinical, and other relevant translational science preceded successful FDA approval of first-in-class cancer drugs, with oncology-related treatments involving fewer median years of target-based research than therapies not related to cancer (33 v 43 years; P < .05). Overall, 94.4% of first-in-class drugs had at least 25 years of target-related research papers, while 85.5% of first-in-class drugs had at least 10 years of translational research publications.

CONCLUSION

Novel first-in-class cancer treatments are defined by diverse clinical indications, personalized molecular targets, dependence on expedited regulatory pathways, and translational research metrics reflecting this complex landscape. Biomedical informatics and AI provide scalable, data-driven ways to assess and even address important challenges in the drug development pipeline.

In Pursuit of Optimal Outcomes: A Framework for Quality Standards in Immune Effector Cell Therapy

Immune effector cell (IEC) therapy represents a transformative advancement in oncology, leveraging the immune system to combat various malignancies. This article outlines a comprehensive framework for establishing and maintaining quality standards in IEC therapy amidst rapid scientific and clinical advancements. We emphasize the integration of structured process measures, robust quality assurance, and meticulous outcome evaluation to ensure treatment efficacy and safety. Key components include multidisciplinary expertise, stringent accreditation protocols, and advanced data management systems, which facilitate standardized reporting and continual innovation. The collaborative effort among stakeholders-ranging from patients and healthcare providers to regulatory bodies-is crucial in delivering high-quality IEC therapies. This framework aims to enhance patient outcomes and cement the role of IEC therapy as a cornerstone of modern oncology, promoting continuous improvement and adherence to high standards across the therapeutic spectrum.

Clinical Outcomes of Patients Treated with Ribociclib in Combination with Aromatase Inhibitors or Fulvestrant for HR-Positive, HER2-Negative Metastatic Breast Cancer, Real-World Data from a Low-Resourced Country

Background

Cyclin-dependent kinase (CDK) 4/6 inhibitors have revolutionized the treatment landscape of hormone receptor-positive (HR+) and human epidermal growth factor receptor 2-negative (HER2 -) metastatic breast cancer (MBC). Here, we present the real-world clinical outcomes and toxicity data of patients treated at a single cancer center.

Methods

A retrospective analysis was conducted on patients with HR+/HER2- MBC treated with ribociclib plus endocrine therapy (ET). Outcomes measured included progression-free survival (PFS), overall survival (OS), and adverse events.

Results

A total of 356 patients (median age 52, range 27-91 years) were enrolled, all with metastatic disease; 204 (57.5%) had de novo metastasis, and 183 (51.4%) had visceral metastasis. Ribociclib was combined with aromatase inhibitors in 321 patients (90.2%) and with fulvestrant in 35 patients (9.8%). Dose reduction was needed in 101 patients (28.4%), primarily due to neutropenia (21.3%) and abnormal liver enzymes (5.9%). After a median follow-up of 36.3 months, median PFS was 27.3 months (95% CI: 21.3-31.7). PFS was significantly better in patients receiving ribociclib as first-line therapy (32.1 months, 95% CI: 27.7-42.1, p < 0.0001) and those with non-visceral metastasis (38.6 months, 95% CI: 29.8-NR, p < 0.0001). Similarly, OS was significantly better in first-line treatment (48.6 months, 95% CI: 39.1-NR) and non-visceral metastasis cases (NR, 95% CI: 40.6-NR, p < 0.0001). No significant differences in 3-year PFS and OS were found between patients with and without dose reductions.

Conclusion

In real-world settings, and away from the stringency of controlled clinical trials, endocrine therapy in combination with ribociclib in patients with HR-positive/HER2-negative MBC is an effective and well-tolerated therapy with a manageable toxicity profile and a low drug discontinuation rate. Dose reduction due to toxicity did not worsen the outcome.

Real-World Evidence Application in Translational Medicine: Making Use of Prescription Claims to Inform Drug-Drug Interactions of a New Psoriasis Treatment

Patients with psoriasis often take multiple medications due to comorbidities, raising concerns about drug-drug interactions (DDIs) during the development of new medicines. DDI risk assessments of a new small molecule showed risks of CYP3A4 autoinduction and being a sensitive CYP3A4 substrate. We conducted a real-world evidence (RWE) claims analysis to assess the frequency of prescription claims for up to 12 months from the date of the initial psoriasis diagnosis for drugs that may interact with CYP3A4 substrates. We used 2013 to 2018 patient data from the US Merative MarketScan Research Database. Among patients diagnosed with psoriasis, less than 1% had a claim for a moderate/strong inducer, but up to 15% had a claim for moderate/strong inhibitor. Most prescriptions for CYP3A4 inhibitors or inducers included antibiotics and anticonvulsants. While CYP3A4 inducers were rarely used, those treated received more than >90 days treatment. Then, these RWE data were used to inform the early translational medicine strategy for the new investigational drug by strategically integrating DDI evaluations into a first-in-human healthy volunteer trial prior to studies in patients with psoriasis. The resulting DDI substudy showed that the investigational small molecule did not induce midazolam clearance but was sensitive to CYP3A inhibition, leading to the decision to exclude concomitant use of strong CYP3A4 inducers or inhibitors from clinical trials.

Real-World Application of a Quantitative Systems Pharmacology (QSP) Model to Predict Potassium Concentrations from Electronic Health Records: A Pilot Case towards Prescribing Monitoring of Spironolactone

Quantitative systems pharmacology (QSP) models are rarely applied prospectively for decision-making in clinical practice. We therefore aimed to operationalize a QSP model for potas-sium homeostasis to predict potassium trajectories based on spironolactone administrations. For this purpose, we proposed a general workflow that was applied to electronic health records (EHR) from patients treated in a German tertiary care hospital. The workflow steps included model exploration, local and global sensitivity analyses (SA), identifiability analysis (IA) of model parameters, and specification of their inter-individual variability (IIV). Patient covariates, selected parameters, and IIV then defined prior information for the Bayesian a posteriori prediction of individual potassium trajectories of the following day. Following these steps, the successfully operationalized QSP model was interactively explored via a Shiny app. SA and IA yielded five influential and estimable parameters (extracellular fluid volume, hyperaldosteronism, mineral corticoid receptor abundance, potassium intake, sodium intake) for Bayesian prediction. The operationalized model was validated in nine pilot patients and showed satisfactory performance based on the (absolute) average fold error. This provides proof-of-principle for a Prescribing Monitoring of potassium concentrations in a hospital system, which could suggest preemptive clinical measures and therefore potentially avoid dangerous hyperkalemia or hypokalemia.

Alzheimer's Disease: Combination Therapies and Clinical Trials for Combination Therapy Development

Alzheimer's disease (AD) is a complex multifaceted disease. Recently approved anti-amyloid monoclonal antibodies slow disease progression by approximately 30%, and combination therapy appears necessary to prevent the onset of AD or produce greater slowing of cognitive and functional decline. Combination therapies may address core features, non-specific co-pathology commonly occurring in patients with AD (e.g., inflammation), or non-AD pathologies that may co-occur with AD (e.g., α-synuclein). Combination therapies may be advanced through co-development of more than one new molecular entity or through add-on strategies including an approved agent plus a new molecular entity. Addressing add-on combination therapy is currently urgent since patients on anti-amyloid monoclonal antibodies may be included in clinical trials for experimental agents. Phase 1 information must be generated for each agent in combination drug development. Phase 2 and Phase 3 of add-on therapies may contrast the new molecular entity, the approved agent as standard of care, and the combination. More complex development programs including standard or modified combinatorial designs are required for co-development of two or more new molecular entities. Biomarkers are markedly affected by anti-amyloid monoclonal antibodies, and these effects must be anticipated in add-on trials. Examining target engagement biomarkers and comparing the magnitude and sequence of biomarker changes in those receiving more than one therapy, compared with those on monotherapy, may be informative. Using network-based medicine approaches, computational strategies may identify rational combinations using disease and drug effect network mapping.

Evolution of Drug Development and Regulatory Affairs: The Demonstrated Power of Artificial Intelligence

PURPOSE

Artificial intelligence (AI) refers to technology capable of mimicking human cognitive functions and has important applications across all sectors and industries, including drug development. This has considerable implications for the regulation of drug development processes, as it is expected to transform both the way drugs are brought to market and the systems through which this process is controlled. There is currently insufficient evidence in published literature of the real-world applications of AI. Therefore, this narrative review investigated, collated, and elucidated the applications of AI in drug development and its regulatory processes.

METHODS

A narrative review was conducted to ascertain the role of AI in streamlining drug development and regulatory processes.

FINDINGS

The findings of this review revealed that machine learning or deep learning, natural language processing, and robotic process automation were favored applications of AI. Each of them had considerable implications on the operations they were intended to support. Overall, the AI tools facilitated access and provided manageability of information for decision-making across the drug development lifecycle. However, the findings also indicate that additional work is required by regulatory authorities to set out appropriate guidance on applications of the technology, which has critical implications for safety, regulatory process workflow and product development costs.

IMPLICATIONS

AI has adequately proven its utility in drug development, prompting further investigations into the translational value of its utility based on cost and time saved for the delivery of essential drugs.

Exploring the discrepancies between clinical trials and real-world data: A small-cell lung cancer study

The potential of real-world data to inform clinical trial design and supplement control arms has gained much interest in recent years. The most common approach relies on reproducing control arm outcomes by matching real-world patient cohorts to clinical trial baseline populations. However, recent studies pointed out that there is a lack of replicability, generalisability, and consensus. In this article, we propose a novel approach that aims to explore and examine these discrepancies by concomitantly investigating the impact of selection criteria and operations on the measurements of outcomes from the patient data. We tested the approach on a dataset consisting of small-cell lung cancer patients receiving platinum-based chemotherapy regimens from a real-world data cohort (n = 223) and six clinical trial control arms (n = 1224). The results showed that the discrepancy between real-world and clinical trial data potentially depends on differences in both patient populations and operational conditions (e.g., frequency of assessments, and censoring), for which further investigation is required. Discovering and accounting for confounders, including hidden effects of differences in operations related to the treatment process and clinical trial study protocol, would potentially allow for improved translation between clinical trials and real-world data. Continued development of the method presented here to systematically explore and account for these differences could pave the way for transferring learning across clinical studies and developing mutual translation between the real-world and clinical trials to inform clinical study design.

Adherence and persistence to tafamidis treatment among Medicare beneficiaries in the presence of a patient assistance program

Tafamidis is the only disease-modifying therapy approved to treat patients in the United States with transthyretin amyloid cardiomyopathy (ATTR-CM), which most commonly affects patients aged ≥ 65 years. The manufacturer operates a patient assistance program (PAP) to support access to tafamidis. This study conducted Privacy Preserving Record Linking (PPRL) using Datavant tokens to match patients across Medicare prescription drug plan (PDP) and PAP databases to evaluate the impact of PAPs on treatment exposure classification, adherence, and persistence determined using Medicare PDP data alone. We found 35% of Medicare PDP patients received tafamidis through the PAP only; 14% through both Medicare PDP and the PAP, and 51% through Medicare PDP only. Adherence and persistence were comparable between these cohorts but underestimated among patients who received ≥ 2 prescriptions through Medicare PDP and ≥ 1 through the PAP when solely using Medicare data versus pooled Medicare and PAP data (modified Medication Possession Ratio: 84% [69% ≥ 80% adherent] vs. 96% [93%]; Proportion of Days Covered: 77% [66% ≥ 80% adherent] vs. 88% [88%]; mean days to discontinuation: 186 vs. 252; total discontinuation: 13% vs. 11%). Cross-database PPRL is a valuable method to build more complete treatment journeys and reduce the risk of exposure misclassification in real-world analyses.

Comparison of Synthetic Data Generation Techniques for Control Group Survival Data in Oncology Clinical Trials: Simulation Study

Background

Synthetic patient data (SPD) generation for survival analysis in oncology trials holds significant potential for accelerating clinical development. Various machine learning methods, including classification and regression trees (CART), random forest (RF), Bayesian network (BN), and conditional tabular generative adversarial network (CTGAN), have been used for this purpose, but their performance in reflecting actual patient survival data remains under investigation.

Objective

The aim of this study was to determine the most suitable SPD generation method for oncology trials, specifically focusing on both progression-free survival (PFS) and overall survival (OS), which are the primary evaluation end points in oncology trials. To achieve this goal, we conducted a comparative simulation of 4 generation methods, including CART, RF, BN, and the CTGAN, and the performance of each method was evaluated.

Methods

Using multiple clinical trial data sets, 1000 data sets were generated by using each method for each clinical trial data set and evaluated as follows: (1) median survival time (MST) of PFS and OS; (2) hazard ratio distance (HRD), which indicates the similarity between the actual survival function and a synthetic survival function; and (3) visual analysis of Kaplan-Meier (KM) plots. Each method's ability to mimic the statistical properties of real patient data was evaluated from these multiple angles.

Results

In most simulation cases, CART demonstrated the high percentages of MSTs for synthetic data falling within the 95% CI range of the MST of the actual data. These percentages ranged from 88.8% to 98.0% for PFS and from 60.8% to 96.1% for OS. In the evaluation of HRD, CART revealed that HRD values were concentrated at approximately 0.9. Conversely, for the other methods, no consistent trend was observed for either PFS or OS. CART demonstrated better similarity than RF, in that CART caused overfitting and RF (a kind of ensemble learning approach) prevented it. In SPD generation, the statistical properties close to the actual data should be the focus, not a well-generalized prediction model. Both the BN and CTGAN methods cannot accurately reflect the statistical properties of the actual data because small data sets are not suitable.

Conclusions

As a method for generating SPD for survival data from small data sets, such as clinical trial data, CART demonstrated to be the most effective method compared to RF, BN, and CTGAN. Additionally, it is possible to improve CART-based generation methods by incorporating feature engineering and other methods in future work.

Montelukast as a repurposable additive drug for standard-efficacy multiple sclerosis treatment: Emulating clinical trials with retrospective administrative health claims data

BACKGROUND

Effective and safe treatment options for multiple sclerosis (MS) are still needed. Montelukast, a leukotriene receptor antagonist (LTRA) currently indicated for asthma or allergic rhinitis, may provide an additional therapeutic approach.

OBJECTIVE

The study aimed to evaluate the effects of montelukast on the relapses of people with MS (pwMS).

METHODS

In this retrospective case-control study, two independent longitudinal claims datasets were used to emulate randomized clinical trials (RCTs). We identified pwMS aged 18-65 years, on MS disease-modifying therapies concomitantly, in de-identified claims from Optum's Clinformatics® Data Mart (CDM) and IQVIA PharMetrics® Plus for Academics. Cases included 483 pwMS on montelukast and with medication adherence in CDM and 208 in PharMetrics Plus for Academics. We randomly sampled controls from 35,330 pwMS without montelukast prescriptions in CDM and 10,128 in PharMetrics Plus for Academics. Relapses were measured over a 2-year period through inpatient hospitalization and corticosteroid claims. A doubly robust causal inference model estimated the effects of montelukast, adjusting for confounders and censored patients.

RESULTS

pwMS treated with montelukast demonstrated a statistically significant 23.6% reduction in relapses compared to non-users in 67.3% of emulated RCTs.

CONCLUSION

Real-world evidence suggested that montelukast reduces MS relapses, warranting future clinical trials and further research on LTRAs' potential mechanism in MS.

Response- and Progression-Based End Points in Trial and Observational Cohorts of Patients With NSCLC

Importance

Response Evaluation Criteria in Solid Tumors (RECIST) are commonly used to assess therapeutic response in clinical trials but not in routine care; thus, RECIST-based end points are difficult to include in observational studies. Clinician-anchored approaches for measuring clinical response have been validated but not widely compared with clinical trial data, limiting their use as evidence for clinical decision-making.

Objective

To compare response- and progression-based end points in clinical trial and observational cohorts of patients with non-small cell lung cancer (NSCLC).

Design, Setting, and Participants

This retrospective cohort study used patient-level data from the IMpower132 trial (conducted April 7, 2016, to May 31, 2017) and a nationwide electronic health record (EHR)-derived deidentified database (data collected January 1, 2011, to March 31, 2022). Patients in the observational cohort were selected according to the inclusion and exclusion criteria of the IMpower132 trial. All patients in the observational cohort had stage IV NSCLC.

Exposure

All patients were randomized to or received first-line carboplatin or cisplatin plus pemetrexed.

Main Outcomes and Measures

End points included response rates, duration of response, and progression-free survival, compared between the trial and observational cohorts before and after weighting. Response rates for the observational cohort were derived from the EHR.

Results

A total of 769 patients met inclusion criteria, 494 in the observational cohort (median [IQR] age, 67 [60-74] years; 228 [46.2%] female; 45 [9.1%] Black or African American; 352 [71.3%] White; 53 [10.7%] American Indian or Alaska Native, Asian, Hawaiian or Pacific Islander, or multiracial) and 275 in the trial cohort (median [IQR] age, 63 [56-68] years; 90 [32.7%] female; 4 [1.5%] Black or African American; 194 [70.5%] White; 65 [23.6%] American Indian or Alaska Native, Asian, Hawaiian or Pacific Islander, or multiracial). All 3 end points were comparable between the study cohorts. Trial patients had a higher number of response assessments compared with patients in the weighted observational cohort. The EHR-derived response rate was numerically higher than the objective response rate after weighting (100.3 of 249.3 [40.2%] vs 105 of 275 [38.2%]) due to higher rates of observed partial response than RECIST-based partial response. Among patients with at least 1 response assessment, the EHR-derived response rate remained higher than the objective response rate (100.3 of 193.4 [51.9%] vs 105 of 256 [41.0%]) due to a higher proportion of patients in the observational cohort with no response assessment.

Conclusions and Relevance

In this study, response- and progression-based end points were similar between clinical trial and weighted observational cohorts, which increases confidence in the reliability of observational end points and can inform their interpretation in relation to trial end points. Additionally, the difference observed in response rates (including vs excluding patients with no response assessment) highlights the importance of future research adopting this 2-way approach when evaluating the relationship of EHR-derived and objective response rates.

Advancing the utilization of real-world data and real-world evidence in clinical pharmacology and translational research-Proceedings from the ASCPT 2023 preconference workshop

Real-world data (RWD) and real-world evidence (RWE) are now being routinely used in epidemiology, clinical practice, and post-approval regulatory decisions. Despite the increasing utility of the methodology and new regulatory guidelines in recent years, there remains a lack of awareness of how this approach can be applied in clinical pharmacology and translational research settings. Therefore, the American Society of Clinical Pharmacology & Therapeutics (ASCPT) held a workshop on March 21st, 2023 entitled "Advancing the Utilization of Real-World Data (RWD) and Real-World Evidence (RWE) in Clinical Pharmacology and Translational Research." The work described herein is a summary of the workshop proceedings.

Real-World Data Quality Framework for Oncology Time to Treatment Discontinuation Use Case: Implementation and Evaluation Study

BACKGROUND

The importance of real-world evidence is widely recognized in observational oncology studies. However, the lack of interoperable data quality standards in the fragmented health information technology landscape represents an important challenge. Therefore, adopting validated systematic methods for evaluating data quality is important for oncology outcomes research leveraging real-world data (RWD).

OBJECTIVE

This study aims to implement real-world time to treatment discontinuation (rwTTD) for a systemic anticancer therapy (SACT) as a new use case for the Use Case Specific Relevance and Quality Assessment, a framework linking data quality and relevance in fit-for-purpose RWD assessment.

METHODS

To define the rwTTD use case, we mapped the operational definition of rwTTD to RWD elements commonly available from oncology electronic health record-derived data sets. We identified 20 tasks to check the completeness and plausibility of data elements concerning SACT use, line of therapy (LOT), death date, and length of follow-up. Using descriptive statistics, we illustrated how to implement the Use Case Specific Relevance and Quality Assessment on 2 oncology databases (Data sets A and B) to estimate the rwTTD of an SACT drug (target SACT) for patients with advanced head and neck cancer diagnosed on or after January 1, 2015.

RESULTS

A total of 1200 (24.96%) of 4808 patients in Data set A and 237 (5.92%) of 4003 patients in Data set B received the target SACT, suggesting better relevance of the former in estimating the rwTTD of the target SACT. The 2 data sets differed with regard to the terminology used for SACT drugs, LOT format, and target SACT LOT distribution over time. Data set B appeared to have less complete SACT records, longer lags in incorporating the latest data, and incomplete mortality data, suggesting a lack of fitness for estimating rwTTD.

CONCLUSIONS

The fit-for-purpose data quality assessment demonstrated substantial variability in the quality of the 2 real-world data sets. The data quality specifications applied for rwTTD estimation can be expanded to support a broad spectrum of oncology use cases.

Establishment of Advanced Regulatory Innovation for Clinical Trials Transformation (ARICTT): a multi-stakeholder public-private partnership-based organization to accelerate the transformation of clinical trials

Clinical trials have evolved with digital technologies and tend towards patient-centricity. A multi-stakeholder approach is needed to address the emerging complexities in clinical trials. In particular, the introduction of digital technologies and an emphasis on patient-centricity are the major trends in clinical trials. In response, we established a public-private partnership-based organization named Advanced Regulatory Innovation for Clinical Trials Transformation (ARICTT). Eleven organizations in total, from academia, industry, and regulatory agencies, participate in ARICTT. Based on multi-stakeholder collaboration from academia, industry, and government/regulatory bodies, we collected and prioritized current topics in clinical trials based on an internal survey. We established a three-year roadmap with axes that were termed trend, goal, structure, theme, topic, and method. In addition, we planned the development of recommendations based on real-world cases with feasibility studies. We developed appropriate organizational structure to fulfill the roadmap of ARICTT. The selected topics were decentralized clinical trials during the first year, followed by the three topics that were awarded the highest priority according to the internal survey: advances in the informed consent process, supporting sites using digital technology, and an effective recruitment strategy. We developed a case-based recommendation paper presenting an overview of the regulatory landscape and practical considerations with explanatory cases. We also designed and conducted fully decentralized trials to evaluate considerations in real-world settings for the selected topics. Overall engagement and communication were supported by the online platform and annual symposiums. In conclusion, we established a multi-stakeholder, public-private partnership-based organization to accelerate the transformation of clinical trials.

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in a revolutionary era in healthcare by individualizing diagnostics and treatments according to each patient's uniquely evolving health status. This groundbreaking method of tailoring disease prevention and treatment considers individual variations in genes, environments, and lifestyles. The goal of precision medicine is to target the "five rights": the right patient, the right drug, the right time, the right dose, and the right route. In this pursuit, in silico techniques have emerged as an anchor, driving precision medicine forward and making this a realistic and promising avenue for personalized therapies. With the advancements in high-throughput DNA sequencing technologies, genomic data, including genetic variants and their interactions with each other and the environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) and pharmacodynamic (PD) data, and mathematical models further contribute to drug optimization, drug behavior prediction, and drug-drug interaction identification. Digital health, wearables, and computational tools offer continuous monitoring and real-time data collection, enabling treatment adjustments. Furthermore, the incorporation of extensive datasets in computational tools, such as electronic health records (EHRs) and omics data, is also another pathway to acquire meaningful information in this field. Although they are fairly new, machine learning (ML) algorithms and artificial intelligence (AI) techniques are also resources researchers use to analyze big data and develop predictive models. This review explores the interplay of these multiple in silico approaches in advancing precision medicine and fostering individual healthcare. Despite intrinsic challenges, such as ethical considerations, data protection, and the need for more comprehensive research, this marks a new era of patient-centered healthcare. Innovative in silico techniques hold the potential to reshape the future of medicine for generations to come.

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions

Background

Social determinants of health (SDoH) like socioeconomics and neighborhoods strongly influence outcomes, yet standardized SDoH data is lacking in electronic health records (EHR), limiting research and care quality.

Methods

We searched PubMed using keywords "SDOH" and "EHR", underwent title/abstract and full-text screening. Included records were analyzed under five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.

Results

We identified 685 articles, of which 324 underwent full review. Key findings include tailored screening instruments implemented across settings, census and claims data linkage providing contextual SDoH profiles, rule-based and neural network systems extracting SDoH from notes using NLP, connections found between SDoH data and healthcare utilization/chronic disease control, and integrated care management programs executed. However, considerable variability persists across data sources, tools, and outcomes.

Discussion

Despite progress identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical to fulfill the potential of SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

The role of early-phase trials and real-world evidence in drug development

Phase 3 randomized controlled trials (RCTs), while the gold standard for treatment efficacy and safety, are not always feasible, are expensive, can be prolonged and can be limited in generalizability. Other under-recognized sources of evidence can also help advance drug development. Basic science, proof-of-concept studies and early-phase RCTs can provide evidence regarding the potential for clinical benefit. Real-world evidence generated from registries or observational datasets can provide insights into the treatment of rare diseases that often pose a challenge for trial recruitment. Pragmatic trials embedded in healthcare systems can assess the treatment effects in clinical settings among patient populations sometimes excluded from trials. This Perspective discusses potential sources of evidence that may be used to complement explanatory phase 3 RCTs and to speed the development of new cardiovascular medications. Content is derived from the 19th Global Cardiovascular Clinical Trialists meeting (December 2022), involving clinical trialists, patients, clinicians, regulators, funders and industry representatives.

Overview of global real-world data sources for pediatric pharmacoepidemiologic research

PURPOSE

Given limited information available on real-world data (RWD) sources with pediatric populations, this study describes features of globally available RWD sources for pediatric pharmacoepidemiologic research.

METHODS

An online questionnaire about pediatric RWD sources and their attributes and capabilities was completed by members and affiliates of the International Society for Pharmacoepidemiology and representatives of nominated databases. All responses were verified by database representatives and summarized.

RESULTS

Of 93 RWD sources identified, 55 unique pediatric RWD sources were verified, including data from Europe (47%), United States (38%), multiregion (7%), Asia-Pacific (5%), and South America (2%). Most databases had nationwide coverage (82%), contained electronic health/medical records (47%) and/or administrative claims data (42%) and were linkable to other databases (65%). Most (71%) had limited outside access (e.g., by approval or through local collaborators); only 10 (18%) databases were publicly available. Six databases (11%) reported having >20 million pediatric observations. Most (91%) included children of all ages (birth until 18th birthday) and contained outpatient medication data (93%), while half (49%) contained inpatient medication data. Many databases captured vaccine information for children (71%), and one-third had regularly updated data on pediatric height (31%) and weight (33%). Other pediatric data attributes captured include diagnoses and comorbidities (89%), lab results (58%), vital signs (55%), devices (55%), imaging results (42%), narrative patient histories (35%), and genetic/biomarker data (22%).

CONCLUSIONS

This study provides an overview with key details about diverse databases that allow researchers to identify fit-for-purpose RWD sources suitable for pediatric pharmacoepidemiologic research.

Alzheimer's Disease Linkage to Real-World Evidence (AD-LINE) Study: Linking Claims Data to Phase 3 GRADUATE Study of Gantenerumab

BACKGROUND

Linking data from clinical trials and real-world claims may improve the robustness of trial data and provide information on the health, economic, and societal impacts of a disease.

OBJECTIVE

To report on the feasibility of linking trial data to Medicare claims data in early symptomatic Alzheimer's disease (AD) in the US.

DESIGN AND SETTING

Alzheimer's Disease Linkage to Real-World Evidence (AD-LINE) was a noninterventional cohort study that included participants recruited from the GRADUATE program whose trial data were linked to their Medicare claims.

PARTICIPANTS

AD-LINE participants were 66 years and older with early symptomatic AD (ie, mild cognitive impairment [MCI] due to AD or mild AD dementia) and were enrolled in the GRADUATE program and a Medicare fee-for-service or Medicare Advantage plan.

MEASUREMENTS

The Centers for Medicare and Medicaid Services linked participants' clinical trial identifiers to their Medicare beneficiary identifiers using a deterministic, exact matching process. Demographics and clinical characteristics of the AD-LINE cohort at baseline were collected. Outcomes measured in this study included healthcare resource utilization derived from Medicare claims data.

RESULTS

In total, 147 participants across 21 US sites were invited to participate and 111 provided informed consent. Of those, 61 patients had linkable data (ie, Medicare beneficiary identifier), Medicare Parts A/B enrollment, and no health maintenance organization (HMO) enrollment in the year before trial entry. Of the 61 participants whose data were analyzed in this study, 30 had MCI due to AD and 31 had mild AD dementia. Participants in the MCI due to AD group had more healthcare resource utilization on average in the baseline period than those in the mild AD dementia group (29.9 [SD, 20.9] vs 24.5 claims [SD, 12.3]). In an ad hoc analysis, a relatively high concordance (85.3%) was seen between the rates of clinically confirmed AD diagnosis and evidence of AD diagnosis in claims data.

CONCLUSION

This linkage process may serve as a proof of concept for researchers interested in linking clinical trial and real-world claims data. The lessons learned from AD-LINE and innovation of data linkage approaches may encourage key stakeholders to link data in the future.

Harnessing artificial intelligence to improve clinical trial design

Zhang et al. discuss how artificial intelligence (AI) can be used to optimize clinical trial design and potentially boost the success rate of clinical trials. AI has unparalleled potential to leverage real-world data and unlock valuable insights for innovative trial design.

Trials and Tribulations - The Challenges of Clinical Trials in Children

In this issue of NEJM Evidence, we see the results of a randomized clinical trial of dapagliflozin or saxagliptin in pediatric type 2 diabetes (T2D). In children and adolescents with T2D, dapagliflozin achieved significant improvements in glycemia in the trial.1 In June 2023, following another pivotal trial, the U.S. Food and Drug Administration (FDA) approved empagliflozin and the combination of empagliflozin and metformin as additions to diet and exercise to improve blood sugar control in children 10 years and older with T2D. Metformin, the only other oral therapy available for the treatment of children with T2D, was first approved for pediatric use in 2000.