Typically, therapeutic proteins (TPs) have a low risk for eliciting meaningful drug interactions (DIs). However, there are select instances where TP drug interactions (TP-DIs) of clinical concern can occur. This white paper discusses the various types of TP-DIs involving mechanisms such as changes in disease state, target-mediated drug disposition, neonatal Fc receptor (FcRn), or antidrug antibodies formation. The nature of TP drug interaction being investigated should determine whether the examination is conducted as a standalone TP-DI study in healthy participants, in patients, or assessed via population pharmacokinetic analysis. DIs involving antibody-drug conjugates are discussed briefly, but the primary focus here will be DIs involving cytokine modulation. Cytokine modulation can occur directly by certain TPs, or indirectly due to moderate to severe inflammation, infection, or injury. Disease states that have been shown to result in indirect disease-DIs that are clinically meaningful have been listed (i.e., typically a twofold change in the systemic exposure of a coadministered sensitive cytochrome P450 substrate drug). Type of disease and severity of inflammation should be the primary drivers for risk assessment for disease-DIs. While more clinical inflammatory marker data needs to be collected, the use of two or more clinical inflammatory markers (such as C-reactive protein, albumin, or interleukin 6) may help broadly categorize whether the predicted magnitude of inflammatory disease-DI risk is negligible, weak, or moderate to strong. Based on current knowledge, clinical DI studies are not necessary for all TPs, and should no longer be conducted in certain disease patient populations such as psoriasis, which do not have sufficient systemic inflammation to cause a meaningful indirect disease-DI.

In order to achieve health equity, we must implement innovative health system, public health, and policy-level interventions to address the historical root causes of structural and institutional racism embedded in our medical and social systems. A history of unconsented medical and research experimentation on vulnerable groups and residual healthcare provider biases toward minoritized patients has led to patient distrust of medical systems and poor quality of care. Historical discriminatory housing and lending policies resulted in racial residential segregation and neighborhoods with inadequate housing, healthy food access, and educational resources, resulting in present-day social determinants of health (SDOH). To reduce these disparities and achieve health equity, four disruptive healthcare innovations are recommended: (i) infuse health equity principles into clinical workflows by implementing National Culturally and Linguistically Appropriate Services Standards; (ii) address poverty-related SDOH; (iii) deliver care and recruit for research in nonclinical settings to reach marginalized communities; and (iv) leverage health system subject matter experts to advocate for health equity policies. During the COVID-19 pandemic we leveraged the diversity of our workforce to deliver bilingual and culturally tailored COVID-19 testing, education, and vaccines to the Hispanic and Black communities in nonclinical settings, the primary marginalized communities served by our health system that were also disproportionately impacted by COVID-19 infections, hospitalizations, and deaths. Now that we understand the importance of using innovative health equity strategies to reach marginalized communities, we must continue to re-engineer our healthcare systems to deliver care outside of our brick and mortar to overcome barriers in access to care and mistrust in the healthcare establishment stemming from past abuses and remaining experiences of bias.

Author information: (1)Data First Consulting, Sebastopol, California, USA. (2)Fred Hutchinson Cancer Research Center, Seattle, Washington, USA. (3)Vitalant Research Institute, San Francisco, California, USA. (4)Edward Grebe Consulting, Cape Town, South Africa. (5)ViiV Healthcare, Research Triangle Park, North Carolina, USA. (6)Gilead Sciences, Foster City, California, USA. (7)University of Washington, Seattle, Washington, USA. (8)Independent Consultant, Washington, DC, USA. (9)Merck & Co., Inc., Kenilworth, New Jersey, USA. (10)Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte, BfArM), Bonn, Germany. (11)Forum for Collaborative Research, Washington, DC, USA.

Among patients treated with irinotecan, homozygous carriers of the UGT1A1*28 allele are at increased risk for neutropenia, but UGT1A1 genotype alone does not account for irinotecan-induced toxicity. Our aim was to study the association between single-nucleotide variants in genes encoding for efflux transporters of irinotecan (ABCG2, ABCB1, and ABCC2) and toxicity in real life. The source population was a cohort of patients with colorectal cancer (CRC) in Northern Israel, who had undergone genome-wide association study. From the source population we chose the patients with CRC prescribed irinotecan, and a comparative cohort of patients with CRC treated with other anticancer systemic therapies. Using Clalit Health Services electronic medical records (including laboratory results) we ascertained hematological and gastrointestinal adverse effects and mortality, within 90 days of the first dose, as a composite outcome. There were 601 patients with CRC who received irinotecan, and 756 patients with CRC treated with other anticancer regimens. The minor allele in rs2231142 (ABCG2) was associated with lower incidence of the composite outcome (odds ratio (OR) = 0.54 (0.33, 0.91); P = 0.02) in irinotecan-treated patients with CRC, but not in patients with CRC treated with other regimens. ABCB1 rs1045642 and ABCC2 rs3740066 were not associated with the composite outcome. In a sensitivity analysis, adjusted for UGT1A1 status and for possible demographic and clinical confounders, adjusted OR was 0.56 (0.33, 0.94) for the association between rs2231142 (ABCG2) and the composite outcome. In conclusion, we describe a novel association between the minor allele of rs2231142 in the efflux transporter gene ABCG2 and protection against severe side effects in CRC patients treating with irinotecan.

Current clinical research does not reflect the diversity of patient populations, despite continued recommendations to increase enrollment of under-represented racial and ethnic groups. The ramifications of this lack of trial diversity are important because of potential differences between races and ethnicities in response to therapies, which have been observed for drugs across indications. Nonrepresentative research populations limit the generalizability of study results, which may lead to questions about safety and efficacy in certain subgroups of patients and hinder regulators, healthcare providers, and patients in their ability to adequately consider the benefits and risks of a therapeutic treatment across all populations. Renewed efforts to address healthcare disparities and increase diversity in clinical trials have demonstrated that inclusive trials are achievable and can provide scientifically rigorous results, and, thus, should stimulate greater action across all stakeholders. Ensuring that studies throughout the clinical development process include representative populations is a scientific imperative to advance health equity, racial justice, and trust in the safety and efficacy of medical therapies. This article reviews the long-standing lack of diversity and barriers to enrollment of diverse and representative populations in clinical trials, outlines the current evolving trial landscape and the efforts of stakeholders, and provides examples from scientifically rigorous inclusive trials. The goal is to share learnings in a wider context of opportunities to enhance diversity, equity, and inclusion in clinical development while ensuring the safety and efficacy of medical therapies in all populations of patients, and in doing so, provide wider patient access to therapeutic treatments.

The population pharmacokinetics (PK) of risankizumab and exposure-response relationships for efficacy and safety in patients with Crohn's disease (CD) were characterized using data from phase II and III studies to support dosing regimen selection. A two-compartment model with first-order absorption and first-order elimination adequately described risankizumab PK. Covariates including sex, baseline fecal calprotectin, corticosteroid use, baseline creatinine clearance, body weight, and baseline albumin were statistically correlated with risankizumab clearance, but their impact on exposure was not clinically relevant for efficacy or safety. Exposure-response analyses showed that exposures associated with the 600 mg intravenous (i.v.) induction dose at Weeks 0, 4, and 8 achieved a near maximal response for all efficacy end points evaluated, with negligible added benefit from the 1,200 mg i.v. regimen. By Week 52 of the maintenance treatment, trends of higher responses were observed for the exposure range associated with the 360 mg subcutaneous (s.c.) every-8-weeks (Q8W) regimen for most of the evaluated efficacy end points, particularly for the more stringent end points, such as endoscopic remission and ulcer-free endoscopy. Exposure-response analyses for safety did not identify any apparent relationship between exposure and safety. These results supported the final dose recommendation of 600 mg i.v. at Weeks 0, 4, and 8, followed by 360 mg s.c. at Week 12 and Q8W thereafter in patients with CD.

Natural language processing (NLP) tools turn free-text notes (FTNs) from electronic health records (EHRs) into data features that can supplement confounding adjustment in pharmacoepidemiologic studies. However, current applications are difficult to scale. We used unsupervised NLP to generate high-dimensional feature spaces from FTNs to improve prediction of drug exposure and outcomes compared with claims-based analyses. We linked Medicare claims with EHR data to generate three cohort studies comparing different classes of medications on the risk of various clinical outcomes. We used "bag-of-words" to generate features for the top 20,000 most prevalent terms from FTNs. We compared machine learning (ML) prediction algorithms using different sets of candidate predictors: Set1 (39 researcher-specified variables), Set2 (Set1 + ML-selected claims codes), and Set3 (Set1 + ML-selected NLP-generated features), vs. Set4 (Set1 + 2 + 3). When modeling treatment choice, we observed a consistent pattern across the examples: ML models utilizing Set4 performed best followed by Set2, Set3, then Set1. When modeling the outcome risk, there was little to no improvement beyond models based on Set1. Supplementing claims data with NLP-generated features from free text notes improved prediction of prescribing choices but had little or no improvement on clinical risk prediction. These findings have implications for strategies to improve confounding using EHR data in pharmacoepidemiologic studies.

Prior to deployment of coronavirus disease 2019 (COVID-19) vaccines in the European Union in 2021, a high vaccine uptake leading to an unprecedented volume of safety data from spontaneous reports and real-world evidence, was anticipated. The European Medicines Agency (EMA) implemented specific activities to ensure enhanced monitoring of emerging vaccine safety information, including intensive monitoring of reports of adverse events of special interest and the use of observed-to-expected analyses. The EMA also commissioned several independent observational studies using a large network of electronic healthcare databases and primary data collection via mobile and web-based applications. This preparedness was key for two high-profile safety signals: thrombosis with thrombocytopenia syndrome (TTS), a new clinical entity associated with adenovirus-vectored vaccines, and myocarditis/pericarditis with messenger RNA vaccines. With no existing case definition nor background rates, the signal of TTS posed particular challenges. Nevertheless, it was rapidly identified, evaluated, contextualized and the risk minimized thanks to close surveillance and an efficient use of available evidence, clinical expertise and flexible regulatory tools. The two signals illustrated the complementarity between spontaneous and real-world data, the former enabling rapid risk identification and communication, the latter enabling further characterization. The COVID-19 pandemic has tremendously enhanced the development of tools and methods to harness the unprecedented volume of safety data generated for the vaccines. Areas for further improvement include the need for better and harmonized data collection across Member States (e.g., stratified vaccine exposure) to support signal evaluation in all population groups, risk contextualization, and safety communication.

The purpose of this study was to characterize the ponesimod effect on the heart rate (HR) in patients with multiple sclerosis (MS). A previous pharmacokinetic (PK) and pharmacodynamic model developed in healthy participants was updated using data from phase II and III trials conducted in patients with MS. Clinically relevant covariates were assessed. Simulations were conducted to evaluate the impact of the lack of adherence to ponesimod treatment and provide guidance in cases of treatment re-initiation. The maximal effect parameter of the PK/HR model was lower in patients with MS (23.5% decrease) compared with healthy volunteers (43.2%). The effect of patient covariates on PK/HR was similar to those identified in healthy participants and not clinically relevant in patients with MS. The population PK/HR model well characterized the effect of ponesimod on the time course of HR in patients with MS. After 2 weeks of treatment with 10 mg or higher doses, the model indicated full tolerance development. After repeated dosing at 20 mg, tolerance was maintained > 60% of the steady-state tolerance for up to 4 days after the last dose. Re-initiating with gradual uptitration is recommended if drug discontinuation lasts ≥ 4 days. This managed the negative chronotropic effects of ponesimod. No bradycardia events were observed within the first 2 weeks of treatment in patients with relapsing MS with a baseline HR > 55 bpm. This justifies the recommendation included in the human prescription drug labeling to monitor HR after the first ponesimod dose in these patients.

Hepatic impairment (HI) is known to modulate drug disposition and may lead to elevated plasma exposure. The aim of this study was to quantitate the in vivo OATP1B-mediated hepatic uptake activity in populations with varying degrees of HI. First, we measured baseline levels of plasma coproporphyrin-I, an endogenous OATP1B biomarker, in an open-label, parallel cohort study in adult subjects with normal liver function and mild, moderate, and severe HI (n = 24, 6/cohort). The geometric mean plasma concentrations of coproporphyrin-I were 1.66-fold, 2.81-fold (P < 0.05), and 7.78-fold (P < 0.0001) higher in mild, moderate, and severe impairment than those healthy controls. Second, we developed a dataset of 21 OATP1B substrate drugs with HI data extracted from literature. Median disease-to-healthy plasma area under the curve (AUC) ratios for substrate drugs were ~ 1.4, 3.0, and 6.4 for mild, moderate, and severe HI, respectively. Additionally, significant linear relationship was noted between AUC ratios of substrate drugs without and with co-administration of rifampin, a prototypic OATP1B inhibitor, and AUC ratios in moderate (P < 0.01) and severe (P < 0.001) HI. Third, a physiologically-based pharmacokinetic model analysis was conducted with 10 substrate drugs following estimation of relative OATP1B functional activity in virtual disease population models using coproporphyrin-I data and verification of substrate models with rifampin drug-drug interaction data. This approach adequately predicted plasma AUC change particularly in moderate (9 of 10 within 2-fold) and severe (5 of 5 within 2-fold) HI. Collective findings indicate progressive reduction, by as much as 90-92%, in OATP1B activity in the HI population.

The US Food and Drug Administration (FDA) guidance has recommended several model-based predictions to determine potential drug-drug interactions (DDIs) mediated by cytochrome P450 (CYP) induction. In particular, the ratio of substrate area under the plasma concentration-time curve (AUCR) under and not under the effect of inducers is predicted by the Michaelis-Menten (MM) model, where the MM constant ( Km ) of a drug is implicitly assumed to be sufficiently higher than the concentration of CYP enzymes that metabolize the drug ( ET ) in both the liver and small intestine. Furthermore, the fraction absorbed from gut lumen ( Fa ) is also assumed to be one because Fa is usually unknown. Here, we found that such assumptions lead to serious errors in predictions of AUCR. To resolve this, we propose a new framework to predict AUCR. Specifically, Fa was re-estimated from experimental permeability values rather than assuming it to be one. Importantly, we used the total quasi-steady-state approximation to derive a new equation, which is valid regardless of the relationship between Km and ET , unlike the MM model. Thus, our framework becomes much more accurate than the original FDA equation, especially for drugs with high affinities, such as midazolam or strong inducers, such as rifampicin, so that the ratio between Km and ET becomes low (i.e., the MM model is invalid). Our work greatly improves the prediction of clinical DDIs, which is critical to preventing drug toxicity and failure.

Therapeutic proteins may first be developed as intravenous (i.v.) therapies with new subcutaneous (s.c.) dosage forms being subsequently developed to provide an alternative route of administration. As of August 2022, there have been 9 therapeutic proteins which were developed as a new s.c. dosage form after the approval of the corresponding i.v. product. This article provides a systematic review of prior experiences in the i.v. to s.c. switch development programs. We describe what types of clinical studies were conducted to support the i.v. to s.c. switch for these nine therapeutic proteins. Publicly available scientific advice from health authorities is summarized, particularly regarding recommendations on overall development strategy, dose selection, immunogenicity assessment, and indication extrapolation. The clinical data from these i.v. to s.c. development programs demonstrate that: (1) when switching from i.v. dosing to s.c. dosing, trough drug concentration (Ctrough ) from s.c. dosing should not be inferior to i.v. dosing with average drug concentration (Cavg ; equivalent to AUC, area under the curve after correcting for dosing intervals between i.v. and s.c. administration) being matched or non-inferior to i.v. dosing; and (2) with appropriate s.c. dose regimens, treatment with s.c. therapeutic proteins can generally achieve similar efficacy and safety as the corresponding i.v. products, suggesting that the much higher maximum concentration (Cmax ) after i.v. infusion as compared with that from s.c. injection is often not relevant to the treatment effect.

Although the population in the United States is diverse, there are disparities in healthcare outcomes in some populations, for example, based on characteristics such as race, ethnicity, sex, gender, age, socioeconomic status, and geographic location. Despite disproportionate healthcare outcomes, certain populations are frequently under-represented in clinical trials intended to support applications requesting US Food and Drug Administration (FDA) approval to market a drug or biologic. Additionally, safety and efficacy of therapeutic products may vary based on intrinsic (e.g., sex, age, race, and ethnicity) and/or extrinsic (e.g., drug interactions and medical practice) factors. Enrolling diverse populations in clinical trials can aid in addressing disparities and better inform the use of medical products in all patients who will use them upon approval. Herein, we outline a few initiatives and activities, such as policy development, regulatory review, regulatory research, and stakeholder engagement, that the FDA has undertaken to promote diversity in clinical trials, to support submission of such information in marketing applications for subgroup analyses, and to communicate information to the public.

Psilocybin is being investigated as a potential treatment for psychiatric and neurological disorders. Only a few studies have evaluated the pharmacokinetics (PKs) of psilocybin and have used body weight-adjusted dosing. Data on PKs and the PK-pharmacodynamic (PD) relationship of fixed doses that are commonly used are unavailable. The present study characterized the PKs and PK-PD relationship of 15, 25, and 30 mg of orally administered psilocybin in 28, 23, and 28 healthy subjects, respectively. Plasma levels of unconjugated psilocin (the psychoactive metabolite of psilocybin) and corresponding subjective effects were repeatedly assessed up to 24 hours. PK parameters were determined using compartmental modeling. Concentration-subjective effect relationships were described using PK-PD modeling. Mean (95% confidence interval) maximal psilocin concentrations were 11 ng/mL (10-13), 17 ng/mL (16-19), and 21 ng/mL (19-24) after the administration of 15, 25, and 30 mg psilocybin, respectively. Maximal concentrations were reached after an average of 2 hours. Elimination half-lives were 1.8 hours (1.7-2.0), 1.4 hours (1.2-1.7), and 1.8 hours (1.6-1.9) for 15, 25, and 30 mg psilocybin, respectively. Mean (± SD) durations of subjective effects were 5.6 ± 2.2 hours, 5.5 ± 1.6 hours, and 6.4 ± 2.2 hours, and maximal effects ("any drug" effects) were 58% ± 25%, 73% ± 27%, and 80% ± 18% after 15, 25, and 30 mg psilocybin, respectively. Psilocin exhibited dose-proportional PKs. The duration and intensity of subjective effects were dose-dependent. Body weight did not influence pharmacokinetics or the response to psilocybin. These data may serve as a reference for future clinical trials.

The anticoagulant warfarin is commonly used to control and prevent thrombotic disorders, such as venous thromboembolism (VTE), which disproportionately afflicts African Americans. Despite the importance of copy number variants (CNVs), few studies have focused on characterizing and understanding their role in drug response and disease risk among African Americans. In this study, we conduct the first genome-wide analysis of CNVs to more comprehensively account for the contribution of genetic variation in warfarin dose requirement and VTE risk among African Americans. We used hidden Markov models to detect CNVs from high-throughput single-nucleotide polymorphism arrays for 340 African American participants in the International Warfarin Pharmacogenetics Consortium. We identified 11,570 CNVs resulting in 2,038 copy number variable regions (CNVRs) and found 3 CNVRs associated with warfarin dose requirement and 3 CNVRs associated with VTE risk in African Americans. CNVRs 1q31.2del and 6q14.1del were associated with increased warfarin dose requirement (β = 11.18 and 4.94, respectively; Pemp  = < 0.002); CNVR 19p13.31del was associated with decreased warfarin dose requirement (β = -1.41, Pemp  = 0.0004); CNVRs (2p22.1del and 5q35.1-q35.2del) were found to be associated with increased risk of VTE (odds ratios (ORs) = 1.88 and 14.9, respectively; Pemp  ≤0.02); and CNVR 10q26.12del was associated with a decreased risk of VTE (OR = 0.6; Pemp  = 0.05). Modeling of the 10q26.12del in HepG2 cells revealed that this deletion results in decreased fibrinogen gene expression, decreased fibrinogen and WDR11 protein levels, and decreased secretion of fibrinogen into the extracellular matrix. We found robust evidence that CNVRs could contribute to warfarin dose requirement and risk of VTE in African Americans and for 10q26.3del describe a possible pathogenic mechanism.

Over the past few decades, genomewide association studies (GWASs) have identified the specific genetics variants contributing to many complex diseases by testing millions of genetic variations across the human genome against a variety of phenotypes. However, GWASs are limited in their ability to uncover mechanistic insight given that most significant associations are found in non-coding region of the genome. Furthermore, the lack of diversity in studies has stymied the advance of precision medicine for many historically excluded populations. In this review, we summarize most popular multi-omics approaches (genomics, transcriptomics, proteomics, and metabolomics) related to precision medicine and highlight if diverse populations have been included and how their findings have advance biological understanding of disease and drug response. New methods that incorporate local ancestry have been to improve the power of GWASs for admixed populations (such as African Americans and Latinx). Because most signals from GWAS are in the non-coding region, other machine learning and omics approaches have been developed to identify the potential causative single-nucleotide polymorphisms and genes that explain these phenotypes. These include polygenic risk scores, expression quantitative trait locus mapping, and transcriptome-wide association studies. Analogous protein methods, such as proteins quantitative trait locus mapping, proteome-wide association studies, and metabolomic approaches provide insight into the consequences of genetic variation on protein abundance. Whereas, integrated multi-omics studies have improved our understanding of the mechanisms for genetic association, we still lack the datasets and cohorts for historically excluded populations to provide equity in precision medicine and pharmacogenomics.

Omalizumab is widely used in clinical practice; however, knowledge gaps in the dosage of omalizumab for children aged 2-6 years with moderate-to-severe persistent allergic asthma have been identified. The aim of this study was to explore dosing regimens for moderately-to-severely allergic pediatric patients aged 2-6 years. The physiologically-based pharmacokinetic (PBPK) model of omalizumab was developed and verified in adult patients, extrapolated to pediatric patients, and simulated for omalizumab by adding two observation chambers (free IgE and total IgE). The simulation results showed that the fold errors of the predicted and observed values of the area under the curve (AUC) and peak plasma concentration (Cmax ) were between 0.5 and 2.0, and the average folding error and the absolute average folding error values for all concentration-time data points were 1.09 and 1.48, respectively. The PBPK model combined with pharmacokinetic/pharmacodynamic analysis of omalizumab demonstrated that both the model-derived dose and the original dose could control the average free IgE of 2-6-year-old children with moderate-to-severe allergic asthma below 25 ng/mL, and some of the model-derived doses were lower. This conclusion provides a basis for the selection of dosage in clinical practice reference.

Clinical trial participant populations fail to adequately represent target populations that drugs are intended to serve. Improving racial and ethnic diversity of clinical trial participants is essential for generalizable, quality clinical research results and ensuring social and medical equity. Site-level clinical research professionals (CRPs) have unique insights on diversity improvement strategies for clinical trial enrollment. A survey was distributed to current CRPs working at clinical research sites in the United States to describe current practices and perceptions of the impact these practices have on participant diversity. Subsequently, descriptive quantitative analysis and inductive content analysis were performed. For the practices surveyed, there are discrepancies between frequency of use and perceived impact on diversity enrollment. Common current practices include phone-based or telemedicine study visits, electronic/digital data collection, and participant compensation. However, we report travel reimbursement and services, translated documents and translator services, and adequate participant compensation as most impactful on diverse enrollment. A multistakeholder approach is necessary to enhance diversity and inclusion (D&I) of study participants. Besides large-scale solutions such as countering community distrust, actionable steps are needed by sponsors and study sites to improve D&I of trial participants. Study leadership at the sponsor, contract research organization (CRO), and site-level should create diversity plans prior to study start, and CRO and sponsor budgets should consider D&I strategies during study planning. Planning should incorporate strategies to improve D&I including adequate participant compensation, translated documents and translator services, and travel reimbursements.