Clinical Pharmacology & Therapeutics最新文献

Pharmacogenomics has shifted drug therapy from trial-and-error to personalized approaches by leveraging genetic biomarkers. However, traditional genotyping assays and short-read sequencing often fail to resolve structural complexity in pharmacogenes, leading to ambiguous haplotypes and incorrect phenotype predictions. To evaluate the potential of emerging long-read sequencing technologies in overcoming these limitations, we analyzed diplotypes and drug response phenotypes across 20 clinically actionable pharmacogenes using Oxford Nanopore or PacBio data from 100 healthy individuals in the 1,000 Genomes Project and 159 participants from the Genomics England cancer and rare disease cohorts, alongside Illumina short-read sequencing data. Long reads achieved phasing accuracies of >98% (Oxford Nanopore) and 96.5% (PacBio), with most genes covered by a single phased haploblock. Variant detection metrics were high (i.e., precision, recall, and F1 > 0.92). Genotype and phenotype concordance between short- and long-read predictions exceeded 99%. Short-read predictions contributed nearly twice as much to overall discordant cases compared to those of long reads. Notably, long reads identified 19 novel star (*) alleles and 106 novel suballeles in nine pharmacogenes, including CYP2D6, CYP2B6, CYP2C9, CYP2C19, CYP4F2, and SLCO1B1, which have been submitted to PharmVar. Additionally, long reads resolved 13 ambiguous CYP2D6 structural variants, including a potentially novel structure. Also, a homozygous TA repeat (UGT1A1*80 + *28) was identified that is associated with a poor metabolizer phenotype. Overall, we reassigned 77 genotypes across nine genes for 58 of the 100 investigated 1,000 Genomes Project subjects. These results demonstrate the superiority of long-read sequencing in phasing and resolving complex genomic regions enabling more precise pharmacogenomic profiling. As sequencing costs decline with rapid technological advances, long-read sequencing may become the method of choice in clinical pharmacogenomics, enhancing therapeutic safety and efficacy.

Exposure-response, or pharmacokinetic–pharmacodynamic (PKPD), analyses support many drug development decisions. It is typically applied without assessment of causality and homogeneity, where the latter refers to the assumption that the reason for variability in exposure is unimportant for the impact on response. Randomized dose is the ideal variable for instrumental variable (IV) analysis that can help determine causal effects. In this work, we present adaptations of two standard IV models, predictor substitution (PS) and control function (CF), to repeated-measures analyses. We compare these to PKPD (PKPDC) models, without (with) correlations between PK and PD random effects, and to a new, partitioned effects (PE), model that allows separate PD relations for dose, covariate, and random effect-driven variability in exposure. Six scenarios simulate situations: (i) without any confounding, (ii) with three different types of confounding, generated through shared underlying variables (protein binding, disease severity, or renal function) between PK and PD, (iii) of an unmeasured active metabolite responsible for driving the response, and (iv) reversed causality. In all but the base case, the PKPD model provided biased parameter estimates that led to inappropriate dose adjustments for response-, concentrations-, or covariate-based dosing. The other models provided adequate estimates for a majority of the scenarios, but only the PE model provided for all scenarios. The PE model also formed the basis for adequate individualization based on concentration or response and can, like CF and PS, be used based on both single and repeated measures.

Exposure-response (E-R) analyses are essential for dose selection in drug development, yet conventional endpoint E-R approaches often overlook concurrent clinical events such as adverse event (AE)-driven dose modifications (DMs), potentially leading to biased or misleading conclusions. In this study, we developed a framework to quantify the impact of AE-driven DMs on endpoint E-R relationships and to explore strategies to improve their accuracy. Using duvelisib as an exemplar, a drug known for frequent DMs due to Grade ≥ 3 infections, pneumonia, or transaminase elevations, we evaluated E-R relationships under three scenarios: ground truth, conventional E-R (based on planned dose exposures), and DM-adjusted E-R (accounting for AE-induced DMs and resultant exposure changes). Our analyses showed that conventional E-R analyses often deviated substantially from the ground truth at frequent DMs, particularly for AEs with delayed onset. Early-onset AEs and their resultant DMs could significantly distort E-R relationships for subsequent AEs. DM-adjusted E-R analyses better approximate the ground truth, especially for late-occurring AEs, but may introduce a risk of overcorrection of early-occurring AEs. These findings highlight the critical need to incorporate the timing and nature of AEs into E-R analyses to ensure robust interpretation, particularly in settings where DMs are frequent.

Post-kala-azar dermal leishmaniasis (PKDL) involves a high macrophage burden in which the Leishmania parasites reside. Liposomal amphotericin B (LAmB) plays a key role in the treatment of PKDL. The mononuclear phagocyte system (MPS) is crucial in the distribution of liposomal drugs as well as the leishmaniasis pathophysiology. This study focused on characterizing the interaction between LAmB pharmacokinetics, the MPS, and parasite dynamics for optimal dosing of LAmB in PKDL. Clinical trial data from the Indian subcontinent, involving short-course LAmB administered alone or with miltefosine, were analyzed using nonlinear mixed-effects modeling. The pharmacokinetics of LAmB were best described by a two-compartment model with a saturable LAmB uptake by the MPS. The maximum MPS uptake capacity was modeled with a baseline component and an additional disease-related component relative to the parasite burden. As treatment progressed, MPS capacity decreased with declining parasite load, resulting in a median 54% increase in the systemic LAmB exposure (AUC_0-24h) by the end of treatment. Simulations suggested that a similar parasite clearance could be achieved with a 50% lower total LAmB dose, supporting the potential efficacy of reduced dosing regimens. Combining LAmB and miltefosine further accelerated parasite clearance compared to LAmB alone. This study highlights the importance of understanding the bidirectional interactions between LAmB pharmacokinetics and parasite infection for interpreting systemic exposure and optimizing treatment approaches. If confirmed in clinical trials, reduced LAmB dosing strategies could enable more rational and cost-effective management of PKDL and other dermal leishmaniases.

Ibuprofen is a commonly used nonsteroidal anti-inflammatory drug, and its injectable form has specific clinical applications. However, ibuprofen injection has not been approved for use in Chinese children. This study aimed to support its approval in this population using a model-informed drug development (MIDD) approach. Ibuprofen injection (Fenliping®) received approval for Chinese pediatric indications in 2019, with a clinical trial waiver based on pediatric extrapolation using modeling and simulation. A physiologically based pharmacokinetic (PBPK) model was used to assess ethnic differences and determine the optimal dose for Chinese children. A single-arm, open-label trial was conducted in 40 pediatric patients aged 0.5–6 years to confirm the appropriateness of the 10 mg/kg dose. Body temperature, pain scores, and adverse events were collected to evaluate efficacy, safety, tolerability, and were used as clinical endpoints for exposure–response analysis. Sparse sampling was applied for pharmacokinetic analysis. A population pharmacokinetic (PopPK) model was developed using clinical data and used to refine the PBPK model and compare pharmacokinetics between Chinese and Caucasian children. Exposure–response analysis evaluated the relationship between exposure and clinical outcomes. The PBPK model showed minimal ethnic impact on pharmacokinetics, supporting a 10 mg/kg dose. In febrile patients, 89.5% achieved temperature < 38.5°C within 4 hours. Pain scores decreased below threshold. One mild drug-related adverse event occurred. PK parameters were comparable across ethnicities, and no exposure–response relationship was observed. The MIDD approach supported full approval of ibuprofen injection in Chinese children. A 10 mg/kg dose was effective, safe, and well-tolerated.

This manuscript describes the scope of implementation and impact of a regulatory agency-qualified panel of six urine biomarkers on drug development, the process for which was conducted and funded by the Critical Path Institute, the Foundation for the National Institutes of Health, and the United States Food and Drug Administration. Since 2018, these qualified kidney injury urine biomarkers have been included in early clinical drug development programs. Drug-induced kidney injury has historically contributed to high drug candidate attrition rates, additional animal testing needed due to the limitations of standard clinical laboratory tests in timely nephrotoxicity detection, and unsustainable development program fiscal costs. By illustrating the practical application, case studies from pharmaceutical companies illustrate how FDA-qualified drug-induced kidney injury biomarkers can enhance early detection and enable more sensitive and/or specific monitoring of nephrotoxicity. Moreover, data from completed trials registered on ClinicalTrials.gov show that the use of these non-standard-of-care biomarkers as prespecified safety endpoints has increased since their qualification in 2018.