CL and Vc differ markedly between patients undergoing hemodialysis and those not undergoing hemodialysis. Dosing nomogram based on these covariate relationships may potentially help in accurate dosing of vancomycin.

f Antiretroviral preexposure prophylaxis (PrEP) with once-daily dosing of tenofovir and tenofovir-emtricitabine was shown to be effective for preventing HIV-1 infection in individuals who had HIV-1-seropositive partners (the Partners PrEP Study). We developed a population pharmacokinetic model for tenofovir and investigated the impacts of different dose reporting methods. Dosing information was collected as patient-reported dosing information (PRDI) from 404 subjects (corresponding to 1,280 drug concentration records) from the main trial and electronic monitoring-based adherence data collected from 211 subjects (corresponding to 327 drug concentration records) in an ancillary adherence study. Model development was conducted with NONMEM (7.2), using PRDI with a steady-state assumption or using PRDI replaced with electronic monitoring records where available. A two-compartment model with first-order absorption was the best model in both modeling approaches, with the need for an absorption lag time when electronic monitoring-based dosing records were included in the analysis. Age, body weight, and creatinine clearance were significant covariates on clearance, but only creatinine clearance was retained in the final models per stepwise selection. Sex was not a significant covariate on clearance. Tenofovir population pharmacokinetic parameter estimates and the precisions of the parameters from the two final models were comparable with the point estimates of the parameters, differing from 0% to 35%, and bootstrap confidence intervals widely overlapped. These findings indicate that PRDI was sufficient for population pharmacokinetic model development in this study, with a high level of adherence per multiple measures.T he nucleotide reverse transcriptase inhibitor (NRTI) tenofovir disoproxil fumarate (TDF) has been used in combination with other antiretroviral agents for the treatment of HIV-1 infection. TDF, the oral prodrug formulation of tenofovir, was approved by the U.S. FDA in 2012 for preexposure prophylaxis (PrEP) in a fixed-dose combination with emtricitabine (FTC) to reduce the risk of sexually acquired HIV-1 among people who are at high risk of HIV infection. The Partners PrEP Study, a phase 3 trial that contributed to the approval, demonstrated the efficacy of TDF alone and in combination with FTC in reducing the risk of HIV-1 acquisition in HIV-1-seronegative members of serodiscordant heterosexual couples (1).Successful HIV-1 prevention depends on individuals' sustained adherence to the medication during periods of risk. Poor drug adherence has been an important challenge in some PrEP trials (2). Tenofovir-containing regimens failed to show efficacy at reducing the risk of acquiring HIV-1 in studies where drug adherence was low as evidenced by undetectable plasma tenofovir concentrations for a large percentage of participants (3, 4). In the Partners PrEP Study, blood samples were drawn during clinic visits to determine drug concentrations in plasma after completion of the study as a measure of drug adherence...

The Partners Demonstration Project was a prospective, open-label, implementation science-driven study of preexposure prophylaxis (PrEP) among heterosexual HIV serodiscordant couples in Kenya and Uganda. Adherence data were collected using the Medication Event Monitoring System (MEMS), and time of sexual activity was collected using the mobile phone short message service (SMS). Two plasma samples were collected at a single study visit. We integrated adherence, pharmacokinetics, and SMS data using a population pharmacokinetic (PopPK) model to simulate tenofovir plasma concentrations from PrEP at the time of sexual activity. In the first stage of this analysis, we used data from the current study to update a prior PopPK model of tenofovir (TFV) developed with data from the Partners PrEP Study (a phase III clinical trial). The second stage involved simulating plasma concentrations at the time of sexual activity using empirical Bayes estimates (EBEs) derived from the final model. In addition, EBEs from a previously published parent metabolite model of TFV (MTN-001, an open-label 3-way crossover study in healthy women) was used to simulate tenofovir diphosphate (TFV-DP) concentrations. We estimated percent PrEP "coverage" as the number of reported sexual events during which simulated concentrations were above an a priori threshold concentrations associated with a high degree of protection from HIV infection: plasma TFV of Ͼ40 ng/ml and peripheral blood mononuclear cell (PBMC) TFV-DP concentration of Ͼ36 fmol/million cells. The levels of coverage were 72% for TFV and 81% for TFV-DP. These levels are consistent with a high degree of protection against HIV acquisition in this study of a pragmatic delivery model for antiretroviral-based HIV prevention.

Background and Objective Trilaciclib is a cyclin-dependent kinase 4/6 inhibitor indicated to decrease the incidence of chemotherapy-induced myelosuppression in patients with extensive-stage small-cell lung cancer. Trilaciclib is a substrate and time-dependent inhibitor of cytochrome P450 3A4 and an inhibitor of multidrug and toxin extrusion 1, multidrug and toxin extrusion 2-K, organic cation transporter 1, and organic cation transporter 2. Here, we investigate the pharmacokinetic drug–drug interaction potential of trilaciclib. Methods Two phase I studies were conducted as prospective, open-label, fixed-sequence drug–drug interaction studies in healthy subjects ( n = 57, n = 20) to investigate potential interactions between intravenously administered trilaciclib (200 or 240 mg/m 2 ) and orally administered midazolam (5 mg), metformin (1000 mg), itraconazole (200 mg), and rifampin (600 mg). A population pharmacokinetic model was fit to phase Ib/IIa data in patients with extensive-stage small-cell lung cancer ( n = 114) to assess the impact of trilaciclib dose and exposure (area under the plasma concentration–time curve) on topotecan clearance. Results Coadministration with trilaciclib had minimal effects on the exposure (area under the plasma concentration–time curve from time 0 to infinity) of midazolam (geometric least-square mean ratio [GMR] vs midazolam alone 1.065; 90% confidence interval [CI] 0.984–1.154) but statistically significantly increased plasma exposure (GMR 1.654; 90% CI 1.472–1.858) and decreased renal clearance (GMR 0.633; 90% CI 0.572–0.701) of metformin. Coadministration of trilaciclib with rifampin or itraconazole decreased trilaciclib area under the plasma concentration–time curve from time 0 to infinity by 17.3% (GMR 0.827; 90% CI 0.785–0.871) and 14.0% (GMR 0.860; 0.820–0.902), respectively, vs trilaciclib alone. Population pharmacokinetic modeling showed no significant effect of trilaciclib on topotecan clearance. Conclusions Overall, the drug–drug interaction and safety profiles of trilaciclib in these studies support its continued use in patients with extensive-stage small-cell lung cancer. Clinical Trial Registration Study 106: EudraCT number: 2019-002303-18; Study 114: not applicable; Study 03: Clinicaltrials.org: NCT02514447; August 2015. Supplementary Information The online version contains supplementary material available at 10.1007/s40261-022-01179-x.

BackgroundDeucravacitinib is a first-in-class, oral, selective, allosteric tyrosine kinase 2 (TYK2) inhibitor approved in multiple countries for the treatment of adults with plaque psoriasis[1,2]. Deucravacitinib binds to the unique TYK2 regulatory domain, conferring greater functional selectivity vs JAK inhibitors, which bind to the catalytic domain. Deucravacitinib showed superior efficacy vs placebo in a phase 2 trial in SLE (NCT03252587)[3].ObjectivesThis analysis assessed the pharmacokinetics (PK), selectivity profile compared to JAK inhibitors, and exposure-response (E-R) relationship for efficacy and safety of deucravacitinib in SLE.MethodsIn the phase 2 trial, patients with active SLE were randomized 1:1:1:1 to placebo or deucravacitinib (3 mg BID, 6 mg BID, 12 mg QD). PK analysis included pooled concentration data from 266 SLE patients and 328 phase 1 participants. IC50was determined byin vitrowhole blood assays and plotted against PK profiles. E-R analyses included data from 356 patients. Logistic regression analyses assessed the relationship between deucravacitinib exposure and probability of achieving efficacy endpoints and safety events at weeks 32 and 48.ResultsDeucravacitinib PK in SLE patients was not meaningfully different from that in phase 1 participants. At 12 mg QD, deucravacitinib Cmaxwas 8-fold lower than JAK 1/3 IC50and 47-fold lower than JAK 2/2 IC50(Figure 1). In the E-R analyses, the probability of achieving SRI(4) and BICLA at week 32 increased with increasing deucravacitinib CminSS, with 3 mg BID providing near-maximal response. The E-R relationship for infection and infestation was relatively flat, while skin and subcutaneous tissue disorders increased with increasing deucravacitinib CminSS. These E-R relationships were similar at week 48.ConclusionDeucravacitinib PK in SLE patients is not meaningfully different from that in phase 1 participants. At clinically relevant exposures, deucravacitinib demonstrates highly selective inhibition of TYK2 vs JAK 1/2/3. The deucravacitinib E-R relationships are well characterized for various efficacy endpoints and safety events.References[1]Armstrong A, et al.J Am Acad Dermatol.2023;88(1):29-39.[2]Strober B, et al.J Am Acad Dermatol.2023;88(1):40-51.[3]Morand E, et al.Arthritis Rheumatol. 2022 Nov 11 (Epub ahead of print).AcknowledgementsThis study was sponsored by Bristol Myers Squibb.Disclosure of InterestsTakafumi Ide Shareholder of: Bristol Myers Squibb, Employee of: Bristol Myers Squibb, Manoj Chiney Shareholder of: Bristol Myers Squibb, Employee of: Bristol Myers Squibb, Huynh Yen Thanh Bach Shareholder of: Bristol Myers Squibb, Employee of: Bristol Myers Squibb, Vineet Goti Shareholder of: Bristol Myers Squibb, Employee of: Bristol Myers Squibb, Bindu Murthy Shareholder of: Bristol Myers Squibb, Employee of: Bristol Myers Squibb, Coburn Hobar Shareholder of: Bristol Myers Squibb, Employee of: Bristol Myers Squibb, Qihong Zhao Shareholder of: Bristol Myers Squibb, Employee of: Bristol Myers Squibb, Urvi Aras Shareholder of: Bristol Myers Squibb, Employee of: Bristol Myers Squibb.