Clinical Pharmacology & Therapeutics最新文献

Patients with rare diseases worldwide face substantial unmet therapeutic needs. In Japan, drug lag—delays in drug approval compared to other countries—has resurfaced as a pressing public health issue. This study analyzes orphan drugs (ODs) approved in the United States (US) from 2005 to 2021, examining OD lag trends, and research and development (R&D) models to streamline OD development in Japan. Despite increased OD approval in the United States since 2018, the number of unapproved ODs in Japan has substantially increased. Although OD lag decreased, it has resurged since 2017. This is largely due to changes in the R&D strategies of pharmaceutical companies, which are driven by the growing presence of US- and Europe-based small- to mid-sized enterprises (SMEs) and the evolving industry landscape. Large foreign pharmaceutical companies have shifted toward a global development strategy for Japan, aiming for more efficient development and competitive advantage. This has been propelled by a move toward in-licensing earlier-stage drug candidates with global exclusivity from SMEs. Japanese pharmaceutical companies have focused on in-licensing late-stage drug candidates for the Japanese market from SMEs without a business presence in Japan, which have not been developed locally, thereby employing a bridging strategy for Japan. With the increase in ODs developed in the United States by these SMEs, this practice has substantially exacerbated the OD lag. As these SMEs are unlikely to enter the Japanese market, it is crucial for Japanese pharmaceutical companies to proactively pursue earlier, more proactive global partnerships with SMEs.

Emergency department visits due to cannabinoid-induced toxicity, including acute cannabinoid intoxication (ACI) have increased worldwide as more states have liberalized cannabis policy. ACI symptoms include anxiety, panic attacks, tachycardia, and psychosis, primarily mediated through cannabinoid type 1 receptor (CB₁) agonism by Δ⁹-tetrahydrocannabinol (THC). This phase II randomized, double-blind, placebo-controlled study assessed the potential of CB₁ receptor antagonist selonabant (ANEB-001) to block THC-induced effects in healthy adults. In Part A of the study, 10.5 mg of THC was coadministered with 50 mg (N = 20) or 100 mg (N = 20) selonabant, or matching placebo (N = 20). In Part B, 21-mg THC was coadministered with 30 mg (N = 9) or 10 mg (N = 7) selonabant, or matching placebo (N = 9). THC-related effects were assessed using visual analogue scales (VAS) for feeling high and alertness, objective measures of postural stability, and heart rate and analyzed using a mixed effects model. Selonabant significantly reduced VAS “Feeling High” (up to −82.8% (95% CI: −91.0%, −67.2%, P < 0.0001) at 30-mg selonabant) and increased VAS “Alertness” (up to 10.8 mm (95% CI: 4.7, 16.8 mm, P = 0.001) at 30-mg selonabant) vs. placebo. Selonabant 10 and 30 mg significantly reduced body sway (up to −30.6% (95% CI: −44.1%, −13.9%, P = 0.002) at 30 mg selonabant) vs. placebo. Effects on heart rate were not significant. Selonabant was generally safe and no clinically meaningful changes in mood occurred. Nausea and vomiting occurred more frequently at high selonabant doses; 10-mg selonabant was both well tolerated and efficacious. Present results support further development of selonabant for emergency treatment of ACI.

With the advancements in algorithms and increased accessibility of multi-source data, machine learning in pharmacokinetics is gaining interest. This review summarizes studies on machine learning-based pharmacokinetics analysis up to September 2024, identified from the PubMed and IEEE Xplore databases. The main focus of this review is on the use of machine learning in predicting drug concentration. This review provides a comprehensive summary of the advances in the machine learning algorithms for pharmacokinetics analysis. Specifically, we describe the common practices in data preprocessing, the application scenarios of various algorithms, and the critical challenges that require attention. Most machine learning models show comparable performance to those of population pharmacokinetics models. Tree-based algorithms and neural networks have the most applications. Furthermore, the use of ensemble modeling techniques can improve the accuracy of these models' predictions of drug concentrations, especially the ensembles of machine learning and pharmacometrics.

The safety of systemic fluoropyrimidines (e.g., 5-fluorouracil, capecitabine) is impacted by germline genetic variants in DPYD, which encodes the dihydropyrimidine dehydrogenase (DPD) enzyme that functions as the rate-limiting step in the catabolism of this drug class. Genetic testing to identify those with DPD deficiency can help mitigate the risk of severe and life-threatening fluoropyrimidine-induced toxicities. Globally, the integration of DPYD genetic testing into patient care has varied greatly, ranging from being required as the standard of care in some countries to limited clinical use in others. Thus, implementation strategies have evolved differently across health systems and countries. The primary objective of this tutorial is to provide practical considerations and best practice recommendations for the implementation of DPYD-guided systemic fluoropyrimidine dosing. We adapted the Exploration, Preparation, Implementation, and Sustainment (EPIS) framework to cover topics including the clinical evidence supporting DPYD genotyping to guide fluoropyrimidine therapy, regulatory guidance for DPYD genotyping, key stakeholder engagement, logistics for DPYD genotyping, development of point-of-care clinical decision support tools, and considerations for the creation of sustainable and scalable DPYD genotype-integrated workflows. This guide also describes approaches to counseling patients about DPYD testing and result disclosure, along with examples of patient and provider educational resources. Together, DPYD testing and clinical practice integration aim to promote safe prescribing of fluoropyrimidine therapy and decrease the risk of severe and life-threatening fluoropyrimidine toxicities.

Ritonavir-boosted atazanavir is a victim of drug–drug interaction with rifampicin, a key component of antitubercular treatment. In a recent dose escalation clinical trial, we showed that increasing atazanavir/ritonavir to 300/100 mg b.i.d. compensates for reduced drug exposure in plasma due to rifampicin, but the intracellular effects remained unexplored. This sub-study investigated the intracellular penetration of atazanavir/ritonavir and dolutegravir into peripheral blood mononuclear cells (PBMC). Twenty-six healthy volunteers living with HIV, virologically suppressed, and taking atazanavir/ritonavir containing regimens were enrolled. The trial consisted of four sequential periods: PK1, participants were on atazanavir/ritonavir 300/100 mg q.d.; at PK2, rifampicin 600 mg q.d. and dolutegravir 50 mg b.i.d. were added (2 weeks); at PK3, atazanavir/ritonavir dose was increased to 300/100 mg b.i.d. (1 week); at PK4, rifampicin dose was doubled (1 week). Atazanavir, ritonavir, and dolutegravir were quantified in plasma and PBMC using LC–MS/MS methods to evaluate steady-state concentrations at the end of each period. Atazanavir/ritonavir dose escalation successfully restored intracellular concentrations comparable to those observed without rifampicin, with a geometric mean ratio of 0.99 (CI₉₀ 0.72–1.41) for atazanavir at PK3 compared with PK1. The intracellular concentration of dolutegravir increased significantly with atazanavir/ritonavir dose escalation, similar to plasma. Finally, further, increasing the rifampicin dose did not show an additional impact on atazanavir/ritonavir concentrations in PBMC. The study confirms that increasing the ATV/r dose can be an effective strategy for compensating rifampicin effects even at the intracellular level, supporting its use in clinical settings.

Obeldesivir is an oral nucleoside analog prodrug inhibitor of SARS-CoV-2 RNA-dependent RNA polymerase and other viral polymerases. Here, two Phase I studies evaluated potential drug–drug interactions between obeldesivir and substrates or inhibitors of cytochrome P450 and drug transporters in healthy participants. When obeldesivir was tested as a precipitant, pharmacokinetic parameter point estimates for midazolam (CYP3A4 inhibition/induction), caffeine (CYP1A2 inhibition), and metformin (organic cation transporter 1 inhibition) exposures were within 80–125% no-effect bounds representing the interval within which a systemic exposure change does not warrant clinical action based on EMA/FDA guidance. Dabigatran (P-glycoprotein substrate) and pitavastatin (organic anion transporting polypeptide 1B1/1B3 substrate) exposures decreased by approximately 25% and 30%, respectively, with obeldesivir coadministration; these were considered not clinically relevant, as these exposure changes are not associated with dose changes or precautions in the US prescribing information for these drugs. When obeldesivir was evaluated as an object, exposures of GS-441524, the parent nucleoside metabolite of obeldesivir, were within the 80–125% no-effect bounds for ritonavir (P-glycoprotein inhibition) and cyclosporin A (breast cancer resistance protein inhibition) coadministration [Correction added on 7 February 2025, after first online publication: The word monophosphate has been removed in this version.]. Famotidine (gastric acid suppression) coadministration decreased GS-441524 exposure by approximately 26%; this was within the range of exposures observed in previous Phase III studies and was considered not clinically relevant. Obeldesivir was well tolerated, and adverse events were mild to moderate. These findings indicate that obeldesivir has low potential for drug–drug interactions. Obeldesivir remains a promising treatment against a broad spectrum of viruses given its antiviral activity and favorable safety profile.

An immunogenicity risk assessment (IRA) is a relatively new expectation of health authorities that is increasingly incorporated into the drug development process across the pharmaceutical/biotech industry. The guiding principle for an IRA includes a comprehensive evaluation of product- and patient-related factors that may influence the immunogenic potential of a biotherapeutic drug and a potential action plan. The Immunogenicity Working Group from the IQ Consortium (Clinical Pharmacology Leadership Group) has conducted a survey to understand the current practices for conducting IRAs and relevant aspects of bioanalysis. Survey results were provided by 19 IQ member companies participating in the Clinical Pharmacology Leadership Group (CPLG) and the Translational and ADME Sciences Leadership Group (TALG). Nearly all the respondents reported experience with monoclonal antibodies (mAb), with 10 other drug modalities including bioengineered protein therapeutics such as fusion and multi-domain proteins, peptides, oligonucleotides as well as gene and cell therapies. The survey results demonstrate that most companies have a defined IRA process, and there was a common understanding that the IRA may need to be revised as more information becomes available or the drug development strategy changes. Some differences found across the respondents are related to the time frame for implementation of IRA document, the types of preclinical data and computational methods used to assess risk, and how the IRA informs clinical plans and documentation practices. These results highlight that while there have been widespread insights gained with performing IRA for mAbs, more experience is needed to perform IRAs for the novel modalities.

Daprodustat, a novel oral hypoxia-inducible factor prolyl hydroxylase inhibitor is approved in the United States for the treatment of anemia due to chronic kidney disease (CKD) in adults receiving dialysis for at least 4 months. Pharmacodynamic dose-hemoglobin (Dose-Hgb) models were developed as daprodustat progressed through development. To support global phase III development, a dose-titration algorithm, guided by simulations from the initial Dose-Hgb model based on phase II clinical data, was implemented. This work was to update and re-calibrate this model to support the dose titration algorithm. Data from five pivotal phase III studies in CKD patients with anemia treated with daprodustat once daily (q.d.) and/or three times a week (t.i.w.) using a titration dosing schedule were included. The data comprised 2,770 CKD patients with anemia providing 53,535 Hgb observations over a period of 6 months up to 4 years. This final Dose-Hgb model consisted of a precursor cell compartment and 12 transit compartments to describe the red blood cell (RBC) lifespan. Treatment increased the precursor cell production rate (K_in) by a power of allometrically scaled dose. Disease progression, as an exponential decline of Hgb production rate over time, varied with dialysis status. The dose-titration algorithm resulted in comparable response for t.i.w. dosing relative to q.d. dosing. Titration-based visual predictive checks for Hgb target criteria for the analysis dataset and the prediction dataset showed that the model adequately predicted the observed data. This re-calibrated Dose-Hgb model will provide further support for the individualized dosing strategy in CKD patients with anemia treated with daprodustat.

In response to increased illicit use of synthetic opioids, various μ-receptor antagonist formulations, with varied pharmacological characteristics, have been and are being developed. To understand how pharmacologic characteristics such as absorption rate and clearance rate affect reversal in treating community opioid overdose, we used our previously published translational opioid model. We adapted this model with in vitro receptor binding data and clinical pharmacokinetic data of three intranasal nalmefene formulations along with an intranasal naloxone formulation to study the reversal of fentanyl and carfentanil-induced respiratory depression in chronic opioid users. Nalmefene has a longer plasma half-life and slower unbinding from the μ-receptor compared to naloxone. For a more rapid reversal of acute overdose-induced respiratory depression, a fast-absorbing antagonist formulation may be of greater utility than a slow-absorbing one containing the same dosage of the antagonist. For preventing renarcotization caused by a long opioid exposure, a slow-clearing antagonist with slow unbinding from the receptor may be of value. While a more potent antagonist with a longer half-life may have the potential to facilitate recovery from respiratory depression for overdose with synthetic opioids, such interventions may also lead to longer and more pronounced withdrawal. This emphasizes the need for a nuanced consideration of several facets while choosing a μ-receptor antagonist, dose, and formulation to treat community opioid overdose cases.

Clopidogrel, an anti-platelet drug, is used to prevent thrombosis after percutaneous coronary intervention. Clopidogrel resistance results in recurring ischemic events, with African Americans (AA) suffering disproportionately. The aim of this study was to discover novel biomarkers of clopidogrel resistance in African Americans using genome and transcriptome data. We conducted a genome-wide association study (GWAS), including local ancestry adjustment, in 141 AA on clopidogrel to identify genetic associations with high on-treatment platelet reactivity (HTPR), with validation of genome-wide significant and suggestive loci in an independent cohort of AA clopidogrel patients (N = 823) from the Million Veteran's Program (MVP) along with in vitro functional analysis. We performed differential gene expression (DGE) analysis in whole blood to identify transcriptomic predictors of response, followed by functional validation in MEG-01 cells. GWAS identified one signal on Chromosome 7 as significantly associated with increasing risk of HTPR. The lead single-nucleotide polymorphism (SNP), rs7807369, within thrombospondin 7A (THSD7A) was associated with an increased risk of HTPR (odds ratio (OR) = 4.02, P = 4.56 × 10⁻⁹). Higher THSD7A gene expression was associated with HTPR in an independent cohort of clopidogrel-treated patients (P = 0.004) and carrying a risk allele showed increased gene expression in primary human endothelial cells. Notably, the CYP2C19*2 variants showed no association with clopidogrel response in the discovery or MVP cohorts. DGE analysis identified an association with decreased LAIR1 and AP3B2 expression to HTPR. LAIR1 knockdown in MEG-01 cells resulted in increased expression of SYK and AKT1, suggesting an inhibitory role of LAIR1 in the Glycoprotein VI pathway. In summary, these findings suggest that other variants and genes outside of CYP2C19 star alleles play an important role in clopidogrel response in AA.