Expert opinion on drug metabolism & toxicology最新文献

Introduction: Dextromethorphan/Buproprion (DXM/BUP) has received breakthrough FDA approval in August 2022 as a rapid-acting antidepressant. DXM/BUP is a noncompetitive N-methyl-D-aspartate (NMDA) receptor antagonist and sigma-1 receptor agonist, with bupropion (BUP), a norepinephrine/dopamine reuptake inhibitor and cytochrome P450 2D6 (CYP2D6) inhibitor. As DXM/BUP moves closer to widespread clinical use for MDD, examining whether the combination does or does not carry meaningful abuse liability is essential given that DXM alone has long been misused, likely attributed to its metabolism into the psychoactive metabolite DXO.

Areas covered: We discuss the pharmacodynamics and pharmacokinetics of DXM and its primary active metabolite, dextrorphan (DXO). We then highlight the abuse potential of DXM when administered alone. Additionally, we present preclinical, clinical, and pharmacovigilance findings that support the reduced abuse liability of DXM/BUP.

Expert opinion: The formulation demonstrates clinically meaningful improvement within one week of its initiation. Given its safety and efficacy, alongside its novelty, this glutamatergic modulator represents a promising candidate for approval across global jurisdictions and regions. Future research should examine DXM/BUP's potential efficacy in bipolar depression and trauma-associated MDD, populations exhibiting refractory responses to conventional antidepressants and in need of an alternative, mechanistically distinct therapeutic.

Introduction: Interindividual variability in drug response remains a significant clinical challenge, leading to therapeutic failure and toxicity. Much of this variability is unexplained by classical host-centric pharmacokinetic (PK) models, highlighting a critical gap in understanding of drug disposition. This review addresses this gap by establishing the gut microbiome as an important determinant of drug fate.

Areas covered: This narrative review with scoping approach examines how microbial enzymes affect therapeutics through comprehensive analysis of mechanistic and clinical studies. Key examples discussed include irinotecan, digoxin, and sulfasalazine. We highlight specific situations where the influence of gut bacteria is particularly significant, such as with low-bioavailability drugs and in patients with an ileocolonic anastomosis, where gut bacteria directly impact drug absorption and metabolism. Additionally, we address the limitations of current PK models and explore the potential of new integrated approaches.

Expert opinion: We propose that the gut microbiome should be recognized as a 'fifth pillar' of PKs. This shift in perspective is crucial for advancing personalized medicine. In this new model, a 'PK profile card' integrating microbial, genomic, and clinical data will help guide dosing. We anticipate microbiome analysis to become a standard clinical tool to optimize drug efficacy and safety.

Introduction: Pharmacological treatment is the mainstay in the acute and long-term management of severe mental disorders such as major depressive disorder, schizophrenia, and bipolar disorder. However, there is large interindividual variability in clinical response, with around one-third of patients presenting treatment-resistance.

Areas covered: This review provides a comprehensive overview of genes that modulate the efficacy or safety of antidepressants, antipsychotics, or mood stabilizers based on a high or moderate level of evidence and for which clinical recommendations are available. Next, we highlight novel methodological and analytical approaches such as polygenic scores, pleiotropic analysis and the analysis of multiomic data with machine learning methods that might allow to explain a larger proportion of genetically driven interindividual variability in clinical response to psychotropic medications.

Expert opinion: To date, a high level of evidence is only available for metabolizer phenotypes of a limited number of pharmacokinetic genes for antidepressants and antipsychotics (CYP2D6, CYP2C19, and CYP2B6), and selected HLA alleles for the mood stabilizer carbamazepine. However, transdiagnostic polygenic scores as well as machine learning models based on the integration of clinical determinants with multiomic data represent a promising strategy to move us closer to precision psychiatry.

Introduction: Predicting drug - drug interactions (DDIs) is essential for safe, effective medication therapy, yet conventional in vitro assays and in silico models are not completely reliable in their assessments. Multi-organ-on-a-chip (MOC) platforms provide a more physiologically relevant approach that may improve in vitro DDI predictions, particularly for complex DDIs.

Areas covered: We outline current DDI workflows, their strengths and limitations, and how single-organ chips can produce quantitative absorption, distribution, metabolism, and excretion (ADME) and toxicity parameters relevant for DDI analysis. We then discuss the need for emerging MOC platforms and the unique advantages that they offer, highlighting case studies that capture more complex DDI scenarios, as well as body-on-a-chip prototypes integrated with mechanistic modeling.

Expert opinion: MOC systems are currently poised to complement, not replace, established in vitro and modeling approaches for DDI predictions. Near-term value lies in fit-for-purpose contexts of use, supplying physiologically grounded parameters and mechanistic insight to physiologically based pharmacokinetic (PBPK) modeling. With continued progress in addressing key challenges (e.g. physiological scaling, sorptive materials, microscale analytics, variability, throughput, and standardization), MOCs should mature into reliable tools to assist in DDI prediction, and potentially even qualified assays as part of regulatory DDI risk assessment frameworks.

Introduction: Drug-drug interactions (DDIs) critically influence drug efficacy and safety, posing risks, but also offering therapeutic opportunities in some circumstances. Their dual nature necessitates balanced strategies in drug development, especially for high-unmet-need areas like oncology.

Areas covered: This review explores DDI risk assessment methods, challenges in correlating preclinical data with clinical outcomes, and advancements in predictive tools like physiologically based pharmacokinetic (PBPK) modeling. In particular, recent new publications highlight innovation such as artificial intelligence (AI) on DDI risk prediction and endogenous biomarkers for noninvasive monitoring. A comprehensive literature search was conducted in PubMed for relevant publications up to Oct 25, 2025.

Expert opinion: Moving beyond a purely defensive stance, we must strategically manage the dual nature of drug-drug interactions across the entire drug lifecycle. This capability is built on a proactive framework that seamlessly integrates multi-faceted data (computational, in vitro, and clinical) to continuously forecast DDI risks and opportunities. The ultimate endpoint of DDI assessment is its clinical impact, quantified through PBPK-DDI-pharmacodynamics (PD) and PBPK-DDI-Toxicity (Tox) models. Rigorous benchmarking of all these predictive methods remains essential to close the translational gap, enhance R&D efficiency, and advance more viable drug candidates.

Introduction: Combined oral contraceptives (COCs), commonly referred to as the 'pill,' are a reliable form of contraception used by a vast majority of women of reproductive potential. COCs contain two primary components: estrogens and progestins. In addition to protection against pregnancy, COCs also confer several significant non-contraceptive benefits, such as the treatment of polycystic ovary syndrome. Given that polypharmacy is a common practice, drug interactions between COCs and concomitant therapies can adversely impact efficacy and safety.

Areas covered: This article systematically reviews literature published up to December 2025, from PubMed and Scopus on drug-metabolizing enzymes and transporters in the disposition of key COC components, drug-COC interactions (DCIs), and clinically relevant safety concerns.

Expert opinion: Progestins are primarily metabolized through phase I enzymes, especially CYP3A4, whereas estrogen metabolism relies on a combination of both phase I and phase II pathways. Nevertheless, the contribution of phase II enzymes to the clearance of COCs remains underappreciated. Much of the existing studies have focused on characterizing metabolic pathways and pharmacokinetic effects rather than assessing meaningful clinical outcomes. While pharmacokinetic interactions can theoretically predict potential adverse effects, robust DCI studies that incorporate real-world clinical outcomes are still critically needed.

Introduction: Letermovir is an antiviral licensed for the prophylaxis of cytomegalovirus (CMV) infection in CMV seropositive allogeneic hematopoietic stem cell transplant (HSCT) recipients. Unlike other anti-CMV agents, letermovir has a favorable toxicity profile. Although the efficacy of letermovir in preventing CMV infection has been demonstrated in clinical trials, virologic failure may occur possibly due to suboptimal letermovir concentrations.

Areas covered: This review summarizes the pharmacology of letermovir with emphasis on factors that can impact letermovir exposure including drug-drug interactions in a population frequently on multiple medications. Studies on letermovir exposure-response relationship are summarized, and the role of therapeutic drug monitoring (TDM) is discussed. A PubMed search using the terms: 'letermovir' and the following terms 'pharmacodynamics,' 'resistances,' 'pharmacokinetics,' 'drug-drug interactions, 'therapeutic drug monitoring' were used to compile data until November 2025.

Expert opinion: Letermovir meets several criteria for TDM, including large interindividual pharmacokinetic variability and exposure-response relationship, albeit, the latter has not been consistently demonstrated. Randomized controlled trials are needed to determine the clinical benefit of letermovir TDM in HSCT recipients. Furthermore, studies are warranted to characterize letermovir pharmacokinetics in situations encountered in clinical practice (complex drug-drug interactions and/or organ dysfunction) and to determine letermovir optimal dosing in the pediatric population.

Introduction: Methotrexate (MTX) is a type of disease-modifying antirheumatic drug and one of the most effective anticancer agents in clinical practices. However, hepatotoxicity and nephrotoxicity are common side effects associated with MTX therapy. Complementary natural medicines for treating hepatorenal toxicity have received widespread research interest.

Area covered: Plant-based natural products have shown promise in managing organ toxicities caused by various drugs. These natural products possess medicinal properties that can be used as a complementary therapy to counteract MTX-induced hepatotoxicity and nephrotoxicity. This study comprehensively reviewed the research on natural products like berberine, gallic acid, resveratrol, etc. that show potential in moderating MTX-induced hepatotoxicity and nephrotoxicity in preclinical models. We explored Google Scholar, Scopus, and PubMed for studies on plant-based natural products and their protective actions against MTX-induced hepatotoxicity and nephrotoxicity. This updated report can be very valuable in deveoping phytotherapeutics for effectively managing the toxicities associated with MTX therapy.

Expert opinion: The studied evidence underscores the promising potential of phytochemicals in mitigating MTX-induced hepatotoxicity and nephrotoxicity via different mechanisms. Given the extensive usage of MTX in various diseases, additional clinical studies are critical to further authenticate these results and optimize phytotherapeutic approaches for enhancing the safety and efficacy of treatment.