Black patients suffer worse outcomes after percutaneous coronary intervention (PCI) than White patients. Inequities in antiplatelet prescribing may contribute to this health disparity. We compared P2Y12 inhibitor prescribing by race following CYP2C19 genotyping to guide antiplatelet therapy selection after PCI. Patients from 9 sites that performed clinical CYP2C19 genotyping after PCI were included. Alternative therapy (e.g., prasugrel or ticagrelor) was recommended for CYP2C19 no-function allele carriers, in whom clopidogrel is predicted to be less effective. The primary outcome was choice of P2Y12 inhibitor (clopidogrel vs. alternative therapy) based on genotype. Of 3,342 patients included, 2,448 (73%) were White, and 659 (20%) were Black. More Black than White patients had a no-function allele (34.3% vs. 29.7%, P = 0.024). At hospital discharge following PCI, 44.2% of Black and 44.0% of White no-function allele carriers were prescribed alternative therapy. At the time of the last follow-up within 12 months, numerically fewer Black (51.8%) than White (56.7%) no-function allele carriers were prescribed alternative therapy (P = 0.190). However, the difference was not significant after accounting for other factors associated with P2Y12 inhibitor selection (odds ratio 0.79, 95% confidence interval 0.58-1.08). Alternative therapy use did not differ between Black (14.3%) and White (16.7%) patients without a no-function allele (P = 0.232). Among real-world patients who received CYP2C19 testing after PCI, P2Y12 inhibitor prescribing rates did not differ between Black and White patients. Our data suggest an absence of racial disparity in genotype-guided antiplatelet prescribing among patients receiving CYP2C19 testing.

Robust prediction of pharmacokinetics (PKs) in pediatric subjects of diverse ages, ethnicities, and morbidities is critical. Qualification of pediatric physiologically-based pharmacokinetic (P-PBPK) models is an essential step toward enabling precision dosing of these vulnerable groups. Twenty-two manuscripts involving P-PBPK predictions and corresponding observed PK data (e.g., area under the curve and clearance) for 22 small-molecule compounds metabolized by CYP (3A4, 1A2, and 2C9), UGT (1A9 and 2B7), FMO3, renal, non-renal, and complex routes were identified; ratios of mean predicted/observed (P/O) PK parameters were calculated. Seventy-eight of 115 mean predicted PK parameters were within 0.8 to 1.25-fold of observed data, 98 within 0.67 to 1.5-fold, 109 within 2-fold, and only 6 P/O ratios were outside of these bounds. A set of 12 CYP3A4-metabolized compounds and a set of 6 metabolized by other enzymes, CYP1A2 (1 compound), CYP2C9 (2 compounds), UGT1A9 (1 compound) and UGT2B7 (2 compounds) had 56 of 59 and 22 of 25 mean P/O ratios, respectively, that fell within the > 0.5 and < 2.0-fold boundaries. For compounds covering renal, non-renal, complex, and FM03 routes of elimination, 29 of 31 mean P/O ratios fell within the 0.67 to 1.5-fold bounds, including 4 of 5 P/O ratios from newborns. P-PBPK modeling and simulation is a strategic component of the complement of precision dosing methods and has a vital role to play in dose adjustment in vulnerable pediatric populations, such as those with disease or in different ethnic groups. Qualification of such models is an essential step toward acceptance of this methodology by regulators.

Immuno-oncology (IO) is a fast-expanding field due to recent success using IO therapies in treating cancer. As IO therapies do not directly kill tumor cells but rather act upon the patients' own immune cells either systemically or in the tumor microenvironment, new and innovative approaches are required to inform IO therapy research and development. Quantitative systems pharmacology (QSP) modeling describes the biological mechanisms of disease and the mode of action of drugs with mathematical equations, which has significant potential to address the big challenges in the IO field, from identifying patient populations that respond to different therapies to guiding the selection, dosing, and scheduling of combination therapy. To assess the perspectives of the community on the impact of QSP modeling in IO drug development and to understand current applications and challenges, the IO QSP working group-under the QSP Special Interest Group (SIG) of the International Society of Pharmacometrics (ISoP)-conducted a survey among QSP modelers, non-QSP modelers, and non-modeling IO program stakeholders. The survey results are presented here with discussions on how to address some of the findings. One of the findings is the differences in perception among these groups. To help bridge this perception gap, we present several case studies demonstrating the impact of QSP modeling in IO and suggest actions that can be taken in the future to increase the real and perceived impact of QSP modeling in IO drug research and development.

Artemisinin-based combination therapies (ACTs) are the primary treatment for malaria. It is essential to characterize the pharmacokinetics (PKs) and pharmacodynamics (PDs) of ACTs in vulnerable populations at risk of suboptimal dosing. We developed a population PK/PD model using data from our previous study of artemether-lumefantrine in HIV-uninfected and HIV-infected children living in a high-transmission region of Uganda. HIV-infected children were on efavirenz-, nevirapine-, or lopinavir-ritonavir-based antiretroviral regimens, with daily trimethoprim-sulfamethoxazole prophylaxis. We assessed selection for resistance in two key parasite transporters, pfcrt and pfmdr1, over 42-day follow-up and incorporated genotyping into a time-to-event model to ascertain how resistance genotype in relation to drug exposure impacts recurrence risk. Two hundred seventy-seven children contributed 364 episodes to the model (186 HIV-uninfected and 178 HIV-infected), with recurrent microscopy-detectable parasitemia detected in 176 episodes by day 42. The final model was a two-compartment model with first-order absorption and an estimated age effect on bioavailability. Systemic lumefantrine exposure was highest with lopinavir-ritonavir, lowest with efavirenz, and equivalent with nevirapine and HIV-uninfected children. HIV status and lumefantrine concentration were significant factors associated with recurrence risk. Significant selection was demonstrated for pfmdr1 N86 and pfcrt K76 in recurrent infections, with no evidence of selection for pfmdr1 Y184F. Less sensitive parasites were able to tolerate lumefantrine concentrations ~ 3.5-fold higher than more sensitive parasites. This is the first population PK model of lumefantrine in HIV-infected children and demonstrates selection for reduced lumefantrine susceptibility, a concern as we confront the threat to ACTs posed by emerging artemisinin resistance in Africa.

The Research to Accelerate Cures and Equity (RACE) for Children Act requires an assessment of molecular targets relevant to pediatric cancer. Due to the biological complexity, candidate molecular targets have been primarily evaluated based on single features such as the presence of mutations or deregulated expression. As the understanding of tumor biology evolves, the relevance of certain molecular targets may need to be assessed at isoform and/or mutation variant level to optimize tailored therapeutic interventions.