A maturity model for the scientific review of clinical trial designs and their informativeness.

Background: Informativeness, in the context of clinical trials, defines whether a study's results definitively answer its research questions with meaningful next steps. Many clinical trials end uninformatively. Clinical trial protocols are required to go through reviews in regulatory and ethical domains: areas that focus on specifics outside of trial design, biostatistics, and research methods.

Totality of evidence of the effectiveness of repurposed therapies for COVID-19: Can we use real-world studies alongside randomized controlled trials?

Rapid and robust strategies to evaluate the efficacy and effectiveness of novel and existing pharmacotherapeutic interventions (repurposed treatments) in future pandemics are required. Observational "real-world studies" (RWS) can report more quickly than randomized controlled trials (RCTs) and would have value were they to yield reliable results. Both RCTs and RWS were deployed during the coronavirus disease 2019 (COVID-19) pandemic.

Coming full circle: The potential utility of real-world evidence to discern predictions from a physiologically based pharmacokinetic model.

Today real word data (RWD) are playing a greater role in informing health care decisions. A physiologically based pharmacokinetic model (PBPK) and observed exposure-risk relationship predicted an increased bleeding risk induced by rivaroxaban (RXB) in patients with mild to moderate chronic kidney disease (CKD) taking concomitant medications that are combined Pgp-CYP3A inhibitors. In this commentary, we explore the potential use of RWD to assess the clinical consequence of this complex drug-drug interaction predicted from PBPK.

Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond.

The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target identification; docking-, fragment-, and motif-based generation of compound libraries; modeling of synthesis feasibility; rank-ordering likely hits according to structural and chemometric similarity to compounds having known activity and affinity to the target(s); optimizing a smaller library for synthesis and high-throughput screening; and combining evidence from screening to support hit-to-lead decisions. Applying AI/ML methods to lead optimization and lead-to-candidate (L2C) decision-making has shown slower progress, especially regarding predicting absorption, distribution, metabolism, excretion, and toxicology properties. The present review surveys reasons why this is so, reports progress that has occurred in recent years, and summarizes some of the issues that remain.