A framework for setting enrollment goals to ensure participant diversity in sponsored clinical trials in the United States.

BACKGROUND: Diversity in clinical trials (CTs) has the potential to improve health equity and close health disparities. Underrepresentation of historically underserved groups compromises the generalizability of trial findings to the target population, hinders innovation, and contributes to low accrual. The aim of this study was to establish a transparent and reproducible process for setting trial diversity enrollment goals informed by the disease epidemiology. METHOD: An advisory board of epidemiologists with expertise in health disparities, equity, diversity, and social determinants of health was convened to evaluate and strengthen the initial goal-setting framework. Data sources used were the epidemiologic literature, US Census, and real-world data (RWD); limitations were considered and addressed where appropriate. A framework was designed to safeguard against the underrepresentation of historically medically underserved groups. A stepwise approach was created with Y/N decisions based on empirical data. RESULTS: We compared race and ethnicity distributions in the RWD of six diseases from Pfizer's portfolio chosen to represent different therapeutic areas (multiple myeloma, fungal infections, Crohn's disease, Gaucher disease, COVID-19, and Lyme disease) to the distributions in the US Census and established trial enrollment goals. Enrollment goals for potential CTs were based on RWD for multiple myeloma, Gaucher disease, and COVID-19; enrollment goals were based on the Census for fungal infections, Crohn's disease, and Lyme disease. CONCLUSIONS: We developed a transparent and reproducible framework for setting CT diversity enrollment goals. We note how limitations due to data sources can be mitigated and consider several ethical decisions in setting equitable enrollment goals.

Novel Methods for Prioritizing "Close-In" Analogs from Structure-Activity Relationship Matrices.

Here we describe the development of novel methods for compound evaluation and prioritization based on the structure-activity relationship matrix (SARM) framework. The SARM data structure allows automatic and exhaustive extraction of SAR patterns from data sets and their organization into a chemically intuitive scaffold/functional-group format. While SARMs have been used in the retrospective analysis of SAR discontinuity and identifying underexplored regions of chemistry space, there have been only a few attempts to apply SARMs prospectively in the prioritization of "close-in" analogs. In this work, three new ways of prioritizing virtual compounds based on SARMs are described: (1) matrix pattern-based prioritization, (2) similarity weighted, matrix pattern-based prioritization, and (3) analysis of variance based prioritization (ANV). All of these methods yielded high predictive power for six benchmark data sets (prediction accuracy R2 range from 0.63 to 0.82), yielding confidence in their application to new design ideas. In particular, the ANV method outperformed the previously reported SARM based method for five out of the six data sets tested. The impact of various SARM parameters were investigated and the reasons why SARM-based compound prioritization methods provide higher predictive power are discussed.

Project-focused activity and knowledge tracker: a unified data analysis, collaboration, and workflow tool for medicinal chemistry project teams.

Advances in the field of drug discovery have brought an explosion in the quantity of data available to medicinal chemists and other project team members. New strategies and systems are needed to help these scientists to efficiently gather, organize, analyze, annotate, and share data about potential new drug molecules of interest to their project teams. Herein we describe a suite of integrated services and end-user applications that facilitate these activities throughout the medicinal chemistry design cycle. The Automated Data Presentation (ADP) and Virtual Compound Profiler (VCP) processes automate the gathering, organization, and storage of real and virtual molecules, respectively, and associated data. The Project-Focused Activity and Knowledge Tracker (PFAKT) provides a unified data analysis and collaboration environment, enhancing decision-making, improving team communication, and increasing efficiency.

Piperazinyl CCR1 antagonists--optimization of human liver microsome stability.

The synthesis, biological activity, and pharmacokinetic profile of CCR1 antagonists are described.

Potent small molecule CCR1 antagonists.

The present manuscript details structure-activity relationship studies of lead structure 1, which led to the discovery of CCR1 antagonists >100-fold more potent than 1.

Novel CCR1 antagonists with improved metabolic stability.

The synthesis, biological activity, and pharmacokinetic profile of novel CCR1 antagonists are described.

CP-481,715, a potent and selective CCR1 antagonist with potential therapeutic implications for inflammatory diseases.

The chemokines CCL3 and CCL5, as well as their shared receptor CCR1, are believed to play a role in the pathogenesis of several inflammatory diseases including rheumatoid arthritis, multiple sclerosis, and transplant rejection. In this study we describe the pharmacological properties of a novel small molecular weight CCR1 antagonist, CP-481,715 (quinoxaline-2-carboxylic acid [4(R)-carbamoyl-1(S)-(3-fluorobenzyl)-2(S),7-dihydroxy-7-methyloctyl]amide). Radiolabeled binding studies indicate that CP-481,715 binds to human CCR1 with a Kd of 9.2 nm and displaces 125I-labeled CCL3 from CCR1-transfected cells with an IC50 of 74 nm. CP-481,715 lacks intrinsic agonist activity but fully blocks the ability of CCL3 and CCL5 to stimulate receptor signaling (guanosine 5'-O-(thiotriphosphate) incorporation; IC50 = 210 nm), calcium mobilization (IC50 = 71 nm), monocyte chemotaxis (IC50 = 55 nm), and matrix metalloproteinase 9 release (IC50 = 54 nm). CP-481,715 retains activity in human whole blood, inhibiting CCL3-induced CD11b up-regulation and actin polymerization (IC50 = 165 and 57 nm, respectively) on monocytes. Furthermore, it behaves as a competitive and reversible antagonist. CP-481,715 is >100-fold selective for CCR1 as compared with a panel of G-protein-coupled receptors including related chemokine receptors. Evidence for its potential use in human disease is suggested by its ability to inhibit 90% of the monocyte chemotactic activity present in 11/15 rheumatoid arthritis synovial fluid samples. These data illustrate that CP-481,715 is a potent and selective antagonist for CCR1 with therapeutic potential for rheumatoid arthritis and other inflammatory diseases.