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This study intended to evaluate the interactions between zamicastat and epoprostenol in healthy human subjects. This was a single‐center, open‐label, two‐period study. In period 1, epoprostenol 8 ng/kg/min was administered alone. In period 2, epoprostenol 8 ng/kg/min was administered following an 8‐day treatment with zamicastat. Since the initial dose of epoprostenol showed to be insufficiently tolerated, it was decreased to 6 ng/kg/min. Blood samples were collected to determine the metabolites of epoprostenol and concentrations of zamicastat and its metabolites. A total of 54 subjects were enrolled and data from 28 subjects were available for pharmacokinetic analysis. The epoprostenol plus zamicastat‐to‐epoprostenol geometric means ratio (GMR) and corresponding 90% confidence interval (CI) for Cav,ss and area under the plasma concentration–time curve from time 0 up to 16 h at steady state (AUC0‐16,ss) of the metabolites of epoprostenol were within the acceptance bioequivalence range (80.00%‐125.00%). The intrasubject coefficient of variation (ISCV) was below 10% for both parameters, on both metabolites. For zamicastat AUC0‐τ,ss, the zamicastat plus epoprostenol‐to‐zamicastat GMR and corresponding 90% CI were within the bioequivalence acceptance range, while for zamicastat Cmax,ss, the lower limit of the 90% CI was slightly below the acceptance range. For zamicastat metabolites, Cmax,ss and AUC0‐τ,ss and the zamicastat plus epoprostenol‐to‐zamicastat GMR were below the acceptance bioequivalence range. ISCV was between 30% and 41% for Cmax,ss and between 21% and 41% for AUC0‐τ,ss, for zamicastat and both metabolites. This study showed that the administration of zamicastat did not significantly modify the cardiovascular effects of epoprostenol and that the interactions between zamicastat and epoprostenol are not expected to be clinically relevant. [ABSTRACT FROM AUTHOR]

It has become increasingly common for patients to rely on the use of multiple prescription medications. The management of polypharmacy requires careful consideration for how drugs are metabolized and their potential for interaction with other drugs. Drug–drug interaction (DDI) assessments are typically performed in a stepwise manner during drug development, though knowledge gaps can exist at the time of approval. The US Food and Drug Administration can establish postmarketing requirements (PMRs) or postmarketing commitments (PMCs) to address these knowledge gaps. In this study, we systematically evaluated PMRs and PMCs established to new molecular entities (NMEs) at the time of initial approval between 2009 and 2023, for the assessment of pharmacokinetics‐based DDIs (i.e., drug metabolizing enzyme‐ and transporter‐related DDIs). We found that 22% of NMEs had at least one DDI‐related PMR or PMC, with a total of 263 that were pharmacokinetic based. Of these, 67% were for the assessment of drug metabolizing enzymes, which were established most frequently for their evaluation as a substrate, and 28% for transporters, which were established most frequently for their evaluation as an inhibitor. The 89% of PMRs and PMCs that were considered fulfilled had a revision to the United States prescribing information, the majority of which resulted in updated new instructions for use. This study highlights the value in conducting PMRs and PMCs early in the drug development process allowing broad patient inclusion at the time of initial drug approval. [ABSTRACT FROM AUTHOR]

The lack of data on drug–drug interactions in pediatrics represents a relevant problem in making appropriate therapeutic decisions. Our study aimed to investigate the incidence and risk factors for potential drug–drug interactions (pDDIs) in pediatric pneumonology units, including cystic fibrosis patients. We performed a 6‐month prospective observational study during which clinical pharmacists, using the Lexicomp Drug Interactions checker, screened medical records to identify pDDIs. Spearman's rank coefficient, logistic regression, and the Mann–Whitney U test were used to identify correlations, analyze risk factors for pDDIs, and compare cystic fibrosis patients with the rest, respectively. Recommendations were provided for the D and X pDDIs categories. Within the 218 patients, 428 pDDIs were identified, out of which 237 were classified as clinically significant. Almost 60% of patients were exposed to at least one relevant interaction. The number of pDDIs correlated with the number of; drugs (rs = 0.53, P <.001), hospitalization length (rs = 0.20, P <.01), and off‐label medicines (rs = 0.25, P <.001). According to the multivariate analysis, at least 6 administered medications (OR = 4.15; 95% CI = 2.21‐7.78), 4 days of hospitalization (OR = 6.41; 95% CI = 2.29‐17.97), and off‐label therapy (OR = 3.37; 95% CI = 1.69‐6.70) were the risk factor for pDDIs. Despite significant differences in the number of medications taken, comorbidities, and off‐label drugs, cystic fibrosis patients were not more exposed to pDDI. Given the lack of data on pDDIs in the pediatric population, the need for close cooperation between clinicians and clinical pharmacists to improve the safety and efficacy of pharmacotherapy is highlighted. [ABSTRACT FROM AUTHOR]

The purpose of this overview was to perform an exploratory analysis of in‐house drug‐drug interaction (DDI) studies conducted with investigational drugs and oral midazolam to assess the value of measuring 1‐OH‐midazolam (1‐OHM) in such studies. The perpetrator effect of the investigational drugs on cytochrome P450 3A (CYP3A) was assessed by analyzing both midazolam and 1‐OHM in plasma and evaluating their pharmacokinetic parameters. Given the almost exclusive metabolism of the parent drug by CYP3A to the main metabolite 1‐OHM, an increase in midazolam and a decrease in 1‐OHM exposure in the case of CYP3A inhibition caused by a perpetrator drug would be expected. The opposite would be anticipated in the case of CYP3A induction. For this analysis, the perpetrator potential of eight different investigational drugs was incorporated. Among the 10 studies included, the identified CYP3A inhibitors (n = 4) and inducers (n = 1) were classified based on the data generated with midazolam per se, with 1‐OHM levels not contributing to the interpretation of the data as they did not corroborate the findings of the parent compound. Therefore, it was concluded that continued analysis of 1‐OHM in plasma may be questionable as it does not add value to the interpretation of the results when performing CYP3A DDI studies with an investigational drug as a perpetrator. [ABSTRACT FROM AUTHOR]

Cilofexor, an oral farnesoid X receptor agonist, and firsocostat, an oral, liver‐targeted inhibitor of acetyl‐coenzyme A carboxylase, are being investigated in combination with semaglutide for the treatment of metabolic dysfunction‐associated steatohepatitis (previously known as nonalcoholic steatohepatitis; NCT04971785). The pharmacokinetics and safety profiles of cilofexor (100 mg) and firsocostat (20 mg) were separately investigated in two phase 1 studies, each of which included healthy Japanese participants (n = 20 in the cilofexor study and n = 21 in the firsocostat study) and non‐Japanese participants (n = 20 in the cilofexor study and n = 21 in the firsocostat study). Intensive pharmacokinetic sampling was performed over 96 h following a single‐dose administration of the study drug. Safety was monitored throughout the study. In total, 39 participants completed each study. The plasma exposures of cilofexor and firsocostat (area under the concentration–time curve [AUC] calculated from time 0 to infinity [AUCinf]) in Japanese participants were 1.24‐fold and 1.98‐fold, respectively, of those in non‐Japanese participants. Both study drugs were well tolerated with no clear differences in adverse events or laboratory abnormalities between Japanese and non‐Japanese participants. The approximate 2‐fold exposure difference of firsocostat between Japanese and non‐Japanese participants at the 20 mg dose does not warrant dose reduction given the previously established safety and tolerability of once‐daily doses of firsocostat up to 200 mg. [ABSTRACT FROM AUTHOR]

This study aims to meticulously map the bibliometric landscape of drug‐drug interactions (DDIs) in clinical research. This represents the first use of bibliometric analysis to comprehensively highlight the evolutionary trends and core themes in this critical field of pharmacology. An exhaustive bibliometric search was performed within the Web of Science Core Collection, aiming to comprehensively gather literature on DDIs in clinical settings. A combination of sophisticated analytical tools including DIKW, VOSviewer, and Citespace was utilized for an in‐depth exploration of bibliometric patterns and trends. Of the 3421 initially identified articles, 2622 were considered relevant. The analysis revealed a marked escalation in DDIs publications, with a peak observed in 2020. Five principal thematic clusters emerged: Safety and Adverse Reactions, Drug Metabolism and Efficacy, Disease and Drug Treatment, Research Methods and Practices, and Special Populations and Combined Medication. Key insights included the escalating significance of drug metabolism in pharmacokinetics, heightened focus on cardiovascular and antiviral therapeutics, and the advancing frontier of personalized medicine. Additionally, the analysis underscored the necessity for strategic attention to vulnerable populations and innovative methodological approaches. This study calls for the global harmonization of research methods in DDIs clinical investigations, advocating for the integration of personalized medicine paradigms and the implementation of cutting‐edge computational analytics. It highlights the imperative for inclusive and collaborative research approaches to adeptly address the intricate challenges of contemporary pharmacotherapy. [ABSTRACT FROM AUTHOR]

As a selective and potent inhibitor targeting the isocitrate dehydrogenase‐2 (IDH2) mutant protein, enasidenib obtained approval from the US Food and Drug Administration (FDA) in 2017 for adult patients with acute myeloid leukemia (AML) with an IDH2 mutation. In vitro investigations demonstrated that enasidenib affects various drug metabolic enzymes and transporters. This current investigation aimed to assess enasidenib on the pharmacokinetics (PKs) of CYP substrates, including dextromethorphan (CYP2D6 probe drug), flurbiprofen (CYP2C9 probe drug), midazolam (CYP3A4 probe drug), omeprazole (CYP2C19 probe drug), and pioglitazone (CYP2C8 probe drug), in patients with AML or myelodysplastic syndrome. Results showed that following the co‐administration of enasidenib (100 mg, once daily) for 28 days, the PK parameters AUC(0‐∞) and Cmax of dextromethorphan increased by 1.37 (90% confidence interval (CI): 0.96, 1.96) and 1.24 (90% CI: 0.94, 1.65)‐fold, respectively, compared to dextromethorphan alone. For flurbiprofen, these parameters increased by 1.14 (90%CI: 1.01, 1.29) and 0.97 (90% CI 0.86, 1.08)‐fold, respectively, when compared to flurbiprofen alone. Conversely, midazolam exhibited decreases to 0.57 (90% CI 0.34, 0.97) and 0.77 (90% CI 0.39, 1.53)‐fold, respectively, in comparison to midazolam alone. The parameters for omeprazole increased by 1.86 (90% CI: 1.33, 2.60) and 1.47 (0.93, 2.31)‐fold, respectively, compared to omeprazole alone, while those for pioglitazone decreased to 0.80 (90% CI: 0.62, 1.03) and 0.87 (90% CI: 0.65, 1.16)‐fold, respectively, in comparison to pioglitazone alone. These findings provide valuable insights into dose recommendations concerning drugs acting as substrates of CYP2D6, CYP2C9, CYP3A4, CYP2C19, and CYP2C8 when administered concurrently with enasidenib. [ABSTRACT FROM AUTHOR]

As the first‐in‐class, selective, and potent inhibitor of the isocitrate dehydrogenase‐2 (IDH2) mutant protein, enasidenib was approved by the US Food and Drug Administration (FDA) in 2017 for the treatment of adult patients with relapsed or refractory acute myeloid leukemia (AML) with an IDH2 mutation. Known for its interactions with various cytochrome P450 (CYP) enzymes and transporters in vitro, a clinical pharmacokinetics (PK) trial was initiated to assess the impact of multiple doses of enasidenib on the single‐dose PK of sensitive probe substrates of several cytochrome P450 enzymes and transporters. In this study, a population pharmacokinetic analysis approach was employed to address challenges posed by high, nonzero baseline caffeine concentrations. Moreover, we integrated full Bayesian inference into this approach innovatively for a more detailed understanding of parameter uncertainty and greater modeling flexibility, alongside Student's t‐distribution for robust error modeling in handling the abnormal outlier caffeine concentration data observed in this trial. Our analyses demonstrated that multiple doses of enasidenib altered caffeine clearance to a clinically meaningful extent, as evidenced by an approximate 8‐fold decrease. This finding led to a specific recommendation in the package insert to avoid the concurrent use of certain CYP1A2 substrates with enasidenib, unless directed otherwise in the prescribing information. Furthermore, this research underlines the technical benefits of integrating full Bayesian inference and incorporating Student's t‐distribution for residual error modeling in the PK field. [ABSTRACT FROM AUTHOR]

Perampanel (PER) is a new type of antiseizure medication used for partial or generalized seizures. However, the plasma concentration shows obvious individual variability in children. The present study aims to ascertain the effect of age, comedications, and cytochrome P450 (CYP) 3A4/5 polymorphisms on PER exposure in Chinese pediatric patients with epilepsy. Clinical data were retrospectively collected in a tertiary children's hospital medical records system from January 2021 to December 2022. The influence factors on the daily dose, plasma concentration, and concentration‐to‐dose ratio (CDR) of PER were investigated. A total of 135 pediatric patients with 178 blood samples were involved. With a median daily dose of 4.0 mg (interquartile range, 3.0‐5.0 mg), the median plasma concentration was 409.4 ng/mL (interquartile range, 251.7‐639.4 ng/mL). The CDR in patients aged less than 4 years was significantly decreased by 48.0% and 39.1% compared with those aged 4‐11 years and 12 years or older, respectively. Enzyme inducers significantly decreased the CDR of PER by 34.5%, while valproic acid showed an increase of 71.7%. In addition, genotype CYP3A5*3/*3 carriers presented a significant increase of 21.5% compared to the CYP3A5*1/*3 expresser. No correlations were observed between the CDR and CYP3A4∗1G polymorphism. PER showed high variations in individual plasma concentrations. Age younger than 4 years, comedication with enzyme inducers or valproic acid, and possession of the CYP3A5*3 genotype potentially predicted PER exposure in pediatric patients with epilepsy. [ABSTRACT FROM AUTHOR]

Statins are widely prescribed and highly susceptible to pharmacokinetic (PK)‐based drug–drug interactions (DDIs). To date, there has not been a comprehensive analysis of the basis upon which statin DDI recommendations in US Food and Drug Administration (FDA) prescribing information (PI) are derived. We have conducted such an analysis. We also assessed the degree of concordance of statin DDI recommendations in FDA PI and those provided in common tertiary clinical resources. We catalogued statin DDI information, including PK data and management recommendations, for statin precipitant drugs approved from 2010 to 2021, available from FDA PI and tertiary clinical resource databases. Recommendations were categorized and mapped with associated PK data to assess consistency in the PK basis for labeling recommendations. From the 80 precipitant drugs evaluated, 180 statin DDIs were identified in FDA PI. Dedicated clinical DDI studies were conducted for 54% (n = 97) of these DDIs and 34% (n = 61) of DDI recommendations were extrapolated from clinical data with other statins. Overall, we found that PK‐based statin recommendations were consistent across PI. These findings highlight regulatory precedence for translating information across statins without conducting dedicated clinical DDI studies, which may support future efforts toward streamlining the approach to investigation and labeling of statin DDIs. In addition, with the exception of some notable discrepancies, general concordance was observed between FDA and tertiary resources regarding "Dose Adjustment" and "Avoid Coadministration" recommendations. However, further analyses are warranted across other DDI pairs to determine whether discordance can routinely lead to different clinical recommendations depending on the drug information resource. [ABSTRACT FROM AUTHOR]

Cannabis‐based therapeutics have garnered increasing attention in recent years as patients seek alternative treatments for various medical conditions. This narrative review provides a comprehensive overview of the science behind the medical use of cannabis, focusing on the medical evidence for commonly treated conditions. In addition, the review addresses the practical considerations of using cannabis as a therapeutic agent, offering insights into dosing strategies, variations in cannabinoid formulation, and individual patient responses. Precautions, adverse consequences, and drug interactions are also discussed, with a focus on patient safety and the potential risks associated with cannabis use. [ABSTRACT FROM AUTHOR]

Maribavir, an orally available antiviral agent, has been approved in multiple countries for the treatment of patients with refractory post‐transplant cytomegalovirus (CMV) infection and/or disease. Maribavir is primarily metabolized by CYP3A4; coadministration with CYP3A4 inducers and inhibitors may significantly alter maribavir exposure, thereby affecting its efficacy and safety. The effect of CYP3A4 inducers and inhibitors on maribavir exposure was evaluated based on a drug‐drug interaction (DDI) study and physiologically‐based pharmacokinetic (PBPK) modeling. The effect of rifampin (a strong inducer of CYP3A4 and moderate inducer of CYP1A2), administered at a 600 mg dose once daily, on maribavir pharmacokinetics was assessed in a clinical phase 1 DDI study in healthy participants. A full PBPK model for maribavir was developed and verified using in vitro and clinical pharmacokinetic data from phase 1 studies. The verified PBPK model was then used to simulate maribavir DDI interactions with various CYP3A4 inducers and inhibitors. The DDI study results showed that coadministration with rifampin decreased the maribavir maximum plasma concentration (Cmax), area under the plasma concentration‐time curve (AUC), and trough concentration (Ctrough) by 39%, 60%, and 82%, respectively. Based on the results from the clinical DDI study, the coadministration of maribavir with rifampin is not recommended. The PBPK model did not predict a clinically significant effect of CYP3A4 inhibitors on maribavir exposure; however, it predicted that strong or moderate CYP3A4 inducers, including carbamazepine, efavirenz, phenobarbital, and phenytoin, may reduce maribavir exposure to a clinically significant extent, and may prompt the consideration of a maribavir dosing increase, in accordance with local approved labels and/or regulations. [ABSTRACT FROM AUTHOR]

Nilotinib is a second‐generation BCR‐ABL tyrosine kinase inhibitor for the treatment of Philadelphia chromosome‐positive chronic myeloid leukemia in both adult and pediatric patients. The pharmacokinetics (PK) of nilotinib in specific populations such as pregnant and lactating people remain poorly understood. Therefore, the objectives of the current study were to develop a physiologically based pharmacokinetic (PBPK) model to predict nilotinib PK in virtual drug‐drug interaction (DDI) studies, as well as in pediatric, pregnant, and lactating populations. The nilotinib PBPK model was built in PK‐Sim, which is part of the free and open‐source software Open Systems Pharmacology. The observed clinical data for the validation of the nilotinib models were obtained from the literature. The model reasonably predicted nilotinib concentrations in the adult population; the DDIs between nilotinib and rifampin or ketoconazole in the adult population; and the PK in the pediatric, pregnant, and lactating populations, although in the latter 2 populations plasma concentrations were slightly underestimated. The ratio of predicted versus observed PK parameters for the adult model ranged from 0.71 to 1.11 for area under the concentration‐time curve and 0.55 to 0.95 for maximum concentration. For the DDI, the predicted area under the concentration‐time curve ratio and maximum concentration ratio fell within the Guest criterion. The current study demonstrated the utility of using PBPK modeling to understand the mechanistic basis of PK differences between adults and specific populations, such as pediatrics, and pregnant and lactating individuals, indicating that this technology can potentially inform or optimize dosing conditions in specific populations. [ABSTRACT FROM AUTHOR]

Glasdegib (DAURISMO) is a hedgehog pathway inhibitor approved for the treatment of acute myeloid leukemia (AML). Cytochrome P450 3A4 (CYP3A4) has been identified as a major metabolism and clearance pathway for glasdegib. The role of CYP3A4 in the clearance of glasdegib has been confirmed with clinical drug–drug interaction (DDI) studies following the coadministration of glasdegib with the strong CYP3A4 inhibitor ketoconazole and the strong inducer rifampin. To evaluate potential drug interactions with CYP3A4 modulators, the coadministration of glasdegib with a moderate CYP3A4 inducer, efavirenz, was evaluated using physiologically based pharmacokinetic (PBPK) modeling using the Simcyp simulator. The glasdegib compound file was developed using measured physicochemical properties, data from human intravenous and oral pharmacokinetics, absorption, distribution, metabolism, and excretion studies, and in vitro reaction phenotyping results. The modeling assumptions, model parameters, and assignments of fractional CYP3A4 metabolism were verified using results from clinical pharmacokinetics (PK) and DDI studies with ketoconazole and rifampin. The verified glasdegib and efavirenz compound files, the latter of which was available in the Simcyp simulator, were used to estimate the potential impact of efavirenz on the PK of glasdegib. PBPK modeling predicted a glasdegib area under the concentration–time curve ratio of 0.45 and maximum plasma concentration ratio of 0.75 following coadministration with efavirenz. The PBPK results, in lieu of a formal clinical study, informed the drug label, with the recommendation to double the clinical dose of glasdegib when administered in conjunction with a moderate CYP3A4 inducer, followed by a resumption of the original dose 7 days post‐discontinuation. [ABSTRACT FROM AUTHOR]

The inhibition of human ether‐a‐go‐go‐related gene (hERG) channels is a known cause of QT prolongation triggered by antipsychotic drugs. Our previous studies suggest that P‐glycoprotein (P‐gp)‐mediated drug interactions may lead to increased gastrointestinal absorption of pimozide and its accumulation in cardiomyocytes, thereby enhancing the inhibitory effect of hERG channels. There is a paucity of epidemiological studies examining the risk of QT prolongation by antipsychotic drugs in terms of P‐gp‐mediated interactions with concomitant drugs. Therefore, using the Japanese Adverse Event Reporting Database, we investigated whether the risk of QT prolongation triggered by antipsychotic drugs associated with hERG inhibition is affected by the concomitant use of selective serotonin reuptake inhibitors (SSRIs) associated with P‐gp inhibition. The results showed that the frequency of QT prolongation increased when the antipsychotic drugs quetiapine and sulpiride, which are P‐gp substrates, were combined with SSRIs with P‐gp inhibition. In contrast, no association with QT prolongation was observed in patients on non‐P‐gp‐substrate antipsychotics, irrespective of the P‐gp inhibitory effect of the concomitant SSRI. These results suggest that P‐gp‐mediated interactions are a risk factor for antipsychotic‐induced QT prolongation. There is a need for further investigation into the risks of specific drug combinations. [ABSTRACT FROM AUTHOR]

Small interfering RNAs (siRNAs) represent a new class of drugs with tremendous potential for battling previously "undruggable" diseases. After nearly 2 decades of efforts in addressing the problems of the poor drug profile of naked unmodified siRNAs, this new modality has finally come to fruition, with 5 agents (patisiran, givosiran, lumasiran, inclisiran, and vutrisiran) being approved since 2018, and with many others in the different phases of clinical development. Unlike small‐molecule drugs and protein therapeutics, siRNAs have different sizes, distinct mechanisms of action, differing physicochemical and pharmacological properties, and, accordingly, a unique pharmacokinetic/pharmacodynamic (PK/PD) relationship. To support the continuous development of siRNAs, it is important to have a thorough and deep understanding of the PK/PD and clinical pharmacology related features of siRNAs. As most of the current siRNA products are conjugated by N‐acetylgalactosamine (GalNAc), this review focuses on the PK/PD relationships and clinical pharmacology of GalNAc‐conjugated siRNAs, including their absorption, distribution, metabolism, excretion (ADME) properties, PK/PD models, drug–drug interactions, clinical pharmacology in special populations, and safety evaluation. In addition, necessary background information related to the development of siRNAs as a therapeutic modality, including the mechanisms of action, the advantages of siRNAs, the problems of naked siRNAs, as well as the strategies used to enhance the clinical utility of siRNAs, have also been covered. The goal of this review is to serve as a "primer" on siRNA PK/PD, and I hope the readers, especially those who have a limited background on siRNA therapeutics, will have a fundamental understanding of siRNA PK/PD and clinical pharmacology after reading this review. [ABSTRACT FROM AUTHOR]

Lasmiditan is an in vitro inhibitor of P‐glycoprotein (P‐gp) and breast cancer resistance protein (BCRP) efflux transporters. We aimed to confirm predictions from physiologically based pharmacokinetic models of lasmiditan, and assess the safety and tolerability of rosuvastatin and dabigatran co‐administered with lasmiditan. In this open‐label, post‐marketing drug–drug interaction, phase 1 clinical trial, eligible participants were adults aged 21‐70 years with a body mass index of 18.5‐35.0 kg/m2. Part 1 (P‐gp, 150 mg dabigatran etexilate with 200 mg lasmiditan) and part 2 (BCRP, 10 mg rosuvastatin with 200 mg lasmiditan) employed similar designs: a single dose of probe substrate administered on day −2 with pharmacokinetic evaluation; 1‐week washout; lasmiditan administered on days 8 and 9 alone; lasmiditan co‐administered with a single dose of probe substrate on day 10, with pharmacokinetic evaluation of probe substrate and lasmiditan. Sixty‐six participants were included in part 1 and 30 participants were included in part 2. Following dabigatran co‐administration with lasmiditan, versus dabigatran alone, 90% confidence intervals for geometric least‐squares (LS) mean ratios of area under the plasma concentration–time curve from time 0 extrapolated to infinity (AUC0–∞) and maximum observed drug concentration (Cmax) were not contained within the non‐effect boundaries (0.80 to 1.25). Dabigatran AUC0–∞ increased by 25% and Cmax increased by 22%. The median time of maximum observed drug concentration (tmax) for dabigatran was 2.0 to 3.0 hours. Following rosuvastatin co‐administration with lasmiditan, versus rosuvastatin alone, 90%CIs for geometric LS mean ratios of AUC0–∞ and Cmax were contained within non‐effect boundaries (0.80‐1.25). Rosuvastatin AUC0–∞ increased by 15% and Cmax increased by 7%. The median tmax for rosuvastatin was 4.0 hours. Results suggest that lasmiditan has a weak effect on P‐gp substrates and no clinically relevant effect on BCRP substrates. [ABSTRACT FROM AUTHOR]

The article discusses the limitations and inconsistencies in drug-drug interaction (DDI) management information provided in product labels, highlighting the potential risks associated with incomplete information.Topics include the shortcomings of DDI information in product labels, the potential risks associated with incomplete information, and the proposed agenda for optimizing DDI management.

The scale‐up of rifampicin‐based prevention regimens is an essential part of the global leprosy strategy. Daily rifampicin may reduce the effectiveness of the oral contraceptive pill (OCP), but little is known about the effects of rifampicin at the less frequent dosing intervals used for leprosy prophylaxis. As many women of reproductive age rely on OCP for family planning, evaluating the interaction with less‐than‐daily rifampicin regimens would enhance the scalability and acceptability of leprosy prophylaxis. Using a semi‐mechanistic pharmacokinetic model of rifampicin induction, we simulated predicted changes in OCP clearance when coadministered with varying rifampicin dosing schedules. Rifampicin given as a single dose (600 or 1200 mg) or 600 mg every 4 weeks was not predicted to result in a clinically relevant interaction with OCP, defined as a >25% increase in clearance. Simulations of daily rifampicin were predicted to increase OCP clearance within the range of observed changes previously reported in the literature. Therefore, our findings suggest that OCP efficacy will be maintained when coadministered with rifampicin‐based leprosy prophylaxis regimens of 600 mg once, 1200 mg once, and 600 mg every 4 weeks. This work provides reassurance to stakeholders that leprosy prophylaxis can be used with OCP without any additional recommendations for contraception prevention. [ABSTRACT FROM AUTHOR]

Mavacamten is a potential inducer of cytochrome P450 (CYP) 3A4 and, as such, could reduce the exposure of the active components of oral contraceptives, ethinyl estradiol (EE) and norethindrone (NOR), where CYP3A4 is involved in metabolism. This study assessed if repeat doses of mavacamten led to a drug–drug interaction with EE and/or NOR. This was an open‐label study in healthy women. In Period 1, participants received 35 mcg of EE and 1 mg of NOR. In Period 2, participants received oral loading doses of mavacamten 25 mg on Days 1–2, 15 mg/day on Days 3‒17, and 35 mcg of EE and 1 mg of NOR on Day 15. Plasma concentrations of mavacamten, EE, and NOR were obtained before dosing and up to 72 hours after dosing. For EE only, a physiologically based pharmacokinetic model was used to simulate mavacamten‐mediated CYP3A4 induction with EE for various CYP2C19 phenotypes. In total, 13 women were enrolled (mean age, 38.9 [standard deviation, 9.65] years). After mavacamten administration, modest increases in area under the concentration–time curves were observed for both EE and NOR. The maximum concentrations and half‐lives for EE and NOR were not affected by coadministration with mavacamten. Criteria for bioequivalence were met or nearly met for EE and NOR exposure with geometric mean ratios between 0.8 and 1.25. All adverse events were mild. The physiologically based pharmacokinetic model predicted a less than 15% decrease in EE exposure across CYP2C19 phenotypes. Coadministration of mavacamten at a therapeutically relevant dose with EE and NOR did not decrease the exposure to either EE or NOR to a level that may lead to reduced effectiveness. [ABSTRACT FROM AUTHOR]

Physiologically based pharmacokinetic (PBPK) models are useful in bridging drug exposure in different ethnic groups, and there is increasing regulatory application of this approach in adults. Reported pediatric PBPK models tend to focus on the North European population, with few examples in other ethnic groups. This study describes the development and verification of a Japanese pediatric PBPK population. The development of the model was based on the existing North European pediatric population. Japanese systems and clinical data were collated from public databases and the literature, and the underlying demographics and equations were optimized so that physiological outputs represented the Japanese pediatric population. The model was tested using 14 different small molecule drugs, eliminated by a variety of pathways, including cytochrome P450 3A4 (CYP3A4) metabolism and renal excretion. Given the limitations of the clinical data, the overall performance of the model was good, with 44/62 predictions for PK parameters (area under the plasma drug concentration–time curve, AUC; maximum serum concentration, Cmax; clearance, CL) being within 0.8‐ to 1.25‐fold, 56/62 within 0.67‐ to 1.5‐fold, and 61/62 within 0.5‐ to 2.0‐fold of the observed values. Specific results for the 5 CYP3A4 substrates showed 20/31 cases were predicted within 0.8‐ to 1.25‐fold, 27/31 within 0.67‐ to 1.5‐fold, and all were within 0.5‐ to 2.0‐fold of the observed values. Given the increased regulatory use of pediatric PBPK in drug development, expanding these models to other ethnic groups are important. Considering qualifying these models based on the context of use, there is a need to expand on the current research to include a larger range of drugs with different elimination pathways. Collaboration among academic, industry, model providers, and regulators will facilitate further development. [ABSTRACT FROM AUTHOR]

Nonsteroidal anti‐inflammatory drugs (NSAIDs) are commonly used for the management of fever, pain, and inflammation. However, they have always been considered to have a double‐faced role, according to their capacity to manage inflammation but also their possible reduction of immune system response and diagnosis delay. This last point could favor a dramatic increase of viral infection diffusion, possibly leading to a more severe outcome. The advent of severe acute respiratory syndrome coronavirus 2 excluded the use of NSAIDs, particularly ibuprofen, and then indicated this drug as the better NSAID to manage infected outpatients and prevent complications. Several authors described the role of NSAIDs and ibuprofen in preventing cytokine storm and modulating the immune system. However, the development of both adverse drug reactions (i.e., gastrointestinal, renal, hepatic, and cardiovascular) and drug interaction recalled the necessity of prescribing the better NSAID for each patient. In this narrative review, we describe the role of NSAIDs, particularly of ibuprofen, in the management of viral symptoms, suggesting that the NSAID may be chosen considering the characteristics of the patient, the comorbidity, and the polytherapy. [ABSTRACT FROM AUTHOR]

Drug‐drug interaction potentials of ensitrelvir, a novel oral inhibitor of 3C‐like protease of severe acute respiratory syndrome coronavirus 2, for drug transporters were evaluated by in vitro and clinical studies. The target drug transporters assessed were P‐glycoprotein (P‐gp), breast cancer resistance protein (BCRP), organic anion transporting polypeptide (OATP) 1B1, OATP1B3, organic anion transporter (OAT) 1, OAT3, organic cation transporter (OCT) 1, OCT2, and multidrug and toxin extrusion 1 and 2K. In vitro study revealed that ensitrelvir is a substrate for P‐gp and BCRP and inhibits P‐gp, BCRP, OATP1B1, OATP1B3, OCT1, and OAT3. Based on these results, a clinical drug‐drug interaction study to evaluate the effect of ensitrelvir on the pharmacokinetics of P‐gp, BCRP, OATP1B1, OATP1B3, and OCT1 substrates was conducted with a cocktail approach using digoxin (P‐gp substrate), rosuvastatin (BCRP, OATP1B1, and OATP1B3 substrate), and metformin (OCT1 substrate). The cocktail was administered first, and after the washout period, the cocktail was coadministered with 500 mg of ensitrelvir. No treatment‐emergent adverse events were observed. Pharmacokinetic analyses demonstrated that the ratios (90% confidence intervals) of "cocktail with ensitrelvir" to "cocktail without ensitrelvir" for maximum plasma concentration and area under the plasma concentration–time curve were, respectively, 2.17 (1.72‐2.73) and 1.31 (1.13‐1.52) for digoxin, 1.97 (1.73‐2.25) and 1.65 (1.47‐1.84) for rosuvastatin, and 1.03 (0.91‐1.16) and 1.02 (0.94‐1.11) for metformin. The results indicate that the exposure levels of digoxin and rosuvastatin increased when coadministered with ensitrelvir, but those of metformin were not changed. In conclusion, ensitrelvir has an impact on the exposure levels of P‐gp, BCRP, OATP1B1, and OATP1B3 substrates. [ABSTRACT FROM AUTHOR]

The aim of the present study is to develop physiologically based pharmacokinetic (PBPK) models for saxagliptin and its active metabolite, 5‐hydroxy saxagliptin, and to predict the effect of coadministration of rifampicin, a strong inducer of cytochrome P450 3A4 enzymes, on the pharmacokinetics of saxagliptin and 5‐hydroxy saxagliptin in patients with renal impairment. The PBPK models of saxagliptin and 5‐hydroxy saxagliptin were developed and validated in GastroPlus for healthy adults with or without rifampicin and adults with varying renal functions. Then, the effect of renal impairment combined with drug‐drug interaction on saxagliptin and 5‐hydroxy saxagliptin pharmacokinetics was investigated. The PBPK models successfully predicted the pharmacokinetics. For saxagliptin, the prediction suggests that rifampin greatly weakened the effect of renal impairment on reducing clearance, and the inductive effect of rifampin on parent drug metabolism seems to be increased with an increase in the degree of renal impairment severity. For patients with the same degree of renal impairment, rifampicin would have a slightly synergistic effect on the increase of 5‐hydroxy saxagliptin exposure compared with dosed alone. There is an unsignificant decline for the saxagliptin total active moiety exposure values in patients with the same degree of renal impairment. It seems that patients with renal impairment are unlikely to require additional dose adjustments when coadministered with rifampicin, compared with saxagliptin alone. Our study provides a reasonable approach to explore unknown DDI potential in renal impairment. [ABSTRACT FROM AUTHOR]

Ritlecitinib, an inhibitor of Janus kinase 3 and hepatocellular carcinoma family kinases, is in development as potential treatment for several inflammatory diseases. In vitro studies presented ritlecitinib as an inhibitor of hepatic organic cation transporter (OCT) 1, renal transporters OCT2 and multidrug and toxin extrusion (MATE) proteins 1/2K using multiple substrates, and ritlecitinib's major inactive metabolite M2, as an inhibitor of OCT1. A clinical interaction study with an OCT1 drug probe (sumatriptan) and relevant probe biomarkers for OCT/MATE was conducted to assess the effect of ritlecitinib on these transporters in healthy adult participants. The selectivity of sumatriptan for OCT1 was confirmed through a series of in vitro uptake assays. A simple static model was used to help contextualize the observed changes in sumatriptan area under the plasma concentration‐time curve (AUC). Coadministration of a single 400‐mg dose of ritlecitinib increased sumatriptan AUC from time 0 to infinity (AUCinf) by ≈30% relative to a single 25‐mg sumatriptan administration alone. When administered 8 hours after a ritlecitinib dose, sumatriptan AUCinf increased by ≈50% relative to sumatriptan given alone. Consistent with OCT1 inhibition, the AUC from time 0 to 24 hours of isobutyryl‐L‐carnitine decreased by ≈15% after ritlecitinib. Based on the evaluation of the renal clearance of N1‐methylnicotinamide, ritlecitinib does not exert clinically meaningful inhibition on renal OCT2 or MATE1/2K. This study confirmed that ritlecitinib and M2 are inhibitors of OCT1 but not OCT2 or MATE1/2K in healthy adults. [ABSTRACT FROM AUTHOR]

Pregnancy is a unique physiological state that results in many changes in bodily function, including cellular, metabolic, and hormonal changes. These changes can have a significant impact on the way small‐molecule drugs and monoclonal antibodies (biologics) function and are metabolized, including efficacy, safety, potency, and adverse effects. In this article, we review the various physiologic changes that occur during pregnancy and their effects on drug and biologic metabolism, including changes in the coagulation, gastrointestinal, renal, endocrine, hepatic, respiratory, and cardiovascular systems. Additionally, we discuss how these changes can affect the processes of drug and biologic absorption, distribution, metabolism, and elimination (pharmacokinetics), and how drugs and biologics interact with biological systems, including mechanisms of drug action and effect (pharmacodynamics) during pregnancy, as well as the potential for drug‐induced toxicity and adverse effects in the mother and developing fetus. The article also examines the implications of these changes for the use of drugs and biologics during pregnancy, including consequences of suboptimal plasma drug concentrations, effect of pregnancy on the pharmacokinetics and pharmacodynamics of biologics, and the need for careful monitoring and individualized drug dosing. Overall, this article aims to provide a comprehensive understanding of the physiologic changes during pregnancy and their effects on drug and biologic metabolism to improve the safe and effective use of drugs. [ABSTRACT FROM AUTHOR]

Following the 2021 World Health Organization's updated recommendations on the management of HIV infection, millions of people living with HIV are currently switched from efavirenz‐based antiretroviral therapy to dolutegravir‐based antiretroviral therapy. Pregnant individuals transitioning from efavirenz to dolutegravir might be at increased risk of insufficient viral suppression in the immediate postswitch period because both efavirenz‐ and pregnancy‐related increases in hormone levels induce enzymes involved in dolutegravir metabolism, namely, cytochrome P450 3A4 and uridine 5′‐diphospho‐glucuronosyltransferase 1A1. This study aimed at developing physiologically based pharmacokinetic models to simulate the switch from efavirenz to dolutegravir in the late second and third trimester. To this end, the drug‐drug interaction between efavirenz and the uridine 5′‐diphospho‐glucuronosyltransferase 1A1 substrates dolutegravir and raltegravir was first simulated in nonpregnant subjects. After successful validation, the physiologically based pharmacokinetic models were translated to pregnancy and dolutegravir pharmacokinetics following efavirenz discontinuation were predicted. Modeling results indicated that, at the end of the second trimester, both efavirenz concentrations and dolutegravir trough concentrations fell below respective pharmacokinetic target thresholds (defined as reported thresholds producing 90%‐95% of the maximum effect) during the time interval from 9.75 to 11 days after dolutegravir initiation. At the end of the third trimester, this time interval spanned from 10.3 days to >4 weeks after dolutegravir initiation. These findings suggest that dolutegravir exposure in the immediate post‐efavirenz switch period during pregnancy may be suboptimal, leading to HIV viremia and, potentially, resistance. The clinical implications of these findings remain to be substantiated by future studies. [ABSTRACT FROM AUTHOR]

Brigatinib is a next‐generation anaplastic lymphoma kinase (ALK) inhibitor approved for the treatment of patients with ALK‐positive (ALK+) non–small cell lung cancer (NSCLC). A phase 1 drug‐drug interaction study was conducted to evaluate the effect of multiple‐dose administration of brigatinib on the single‐dose pharmacokinetics of midazolam, a sensitive cytochrome P450 3A substrate. In cycle 1, patients with ALK+ or ROS1+ solid tumors, including NSCLC, received a single 3‐mg dose of midazolam as an oral solution alone on day 1 and then coadministered with brigatinib on day 21 (brigatinib 90 mg once daily on days 2‐8; 180 mg once daily on days 9‐28). After cycle 1, patients could continue to receive brigatinib in 28‐day treatment cycles. The primary study objective was to characterize the effect of brigatinib 180 mg once daily on midazolam pharmacokinetics. The secondary objective was to assess safety. Exploratory efficacy endpoints included objective response rate and progression‐free survival. Brigatinib was generally well tolerated, and safety data were consistent with the known safety profile. Among the 10 patients with ALK+ NSCLC, the confirmed objective response rate was 30% and median progression‐free survival was 7.2 months. Coadministration of brigatinib reduced midazolam maximum observed plasma concentration by ≈16% (geometric least‐squares mean ratio, 0.836 [90%CI, 0.662‐1.056]) and area under the plasma concentration–time curve from time 0 to infinity by ≈26% (geometric least‐squares mean ratio, 0.741 [90%CI, 0.600‐0.915]). Thus, brigatinib is a weak inducer of cytochrome P450 3A in vivo. [ABSTRACT FROM AUTHOR]

Direct oral anticoagulants have been an increasingly used class of drugs in the setting of nonvalvular atrial fibrillation, defying vitamin K antagonists' monopoly when it comes to anticoagulation due to its several limitations. Direct oral anticoagulants (DOACs) have entered the market as a noninferior and safer option in comparison with vitamin K antagonists, as their respective phase III clinical trials proved. The aim of this article was to update and summarize data on their clinical pharmacology and to review real‐world data to know their comparative effectiveness and safety. We performed a systematic review using PubMed, Google Scholar, Embase, and Web of Science as search engines. Regarding pharmacodynamics, there were no substantial changes reported from their original profile. There were many advances in the knowledge about clinical pharmacokinetics of DOACs that have had a direct impact on their clinical use, mainly related to drug‐drug interactions. In a real‐world setting, DOACs have shown to be noninferior in preventing thromboembolic events compared to vitamin K antagonists. In regards to safety, DOACs have shown a lower bleeding risk relative to warfarin. Comparison between DOACs has demonstrated rivaroxaban to have the highest bleeding risk. Overall, the evidence gathered showed few changes from the original data presented in phase III clinical trials, concluding that their real‐world use coincides greatly with them. [ABSTRACT FROM AUTHOR]

Favipiravir is one of the most used antiviral agents for the treatment of coronavirus disease 2019 infection in many countries, including Thailand. This study aimed to investigate the effect of favipiravir‐warfarin interaction in terms of changes in international normalized ratio (INR) of patients. Medication charts of all inpatients in a hospital in Thailand between April 2021 and March 2022 were reviewed. Patients who received either warfarin with standard care or warfarin with favipiravir were included. The INR levels of patients were monitored at baseline and the earliest date following treatment, as well as other laboratory parameters. There were 43 and 53 patients in the warfarin‐favipiravir and the warfarin‐only groups, respectively. Baseline characteristics, such as sex, age, body mass index, and warfarin dose, were not significantly different between the 2 groups. The results showed that the mean INR of patients using favipiravir and warfarin was increased from 2.14 to 3.88 (P <.001), while the patients using warfarin alone had no increase in the mean INR (1.93 vs 1.91; P =.906). Other parameters were not significantly changed, including white blood cell count, red blood cell count, hemoglobin, hematocrit, and liver function. However, an increase in platelet count was observed in the favipiravir‐warfarin group, but not in the control group. This real‐world study highlighted a significant increase in the INR levels of patients who used favipiravir together with warfarin, compared to patients who used only warfarin. However, the interaction did not affect other laboratory parameters, except an increase in platelet count. [ABSTRACT FROM AUTHOR]

SHR4640 is a novel, selective urate reabsorption inhibitor. As the mode of action of SHR4640 differs from that of a xanthine oxidase inhibitor, such as febuxostat, coadministration of these drugs may be a treatment option for patients with primary hyperuricemia. We assessed the potential drug-drug interaction between SHR4640 and febuxostat. In this single-center, open-label, randomized, drug-drug interaction study, subjects received 80 mg febuxostat or 10 mg SHR4640 alone daily in the first week, whereas during the second week a combination of SHR4640 and febuxostat was administered daily to all subjects. Plasma concentrations of SHR4640 and febuxostat were analyzed. We compared their pharmacokinetic and pharmacodynamic parameters and assessed both safety and tolerability. Compared with febuxostat alone, the geometric mean ratios (90%CIs) of the maximum concentration (Cmax) and the area under the plasma concentration-time curve over the dosing interval t (AUC0-t) for febuxostat after coadministration were 1.284 (1.016 to 1.621) and 0.984 (0.876 to 1.106), respectively. The geometric mean ratios (90%CIs) of Cmax and AUC0-t for SHR4640 after coadministration compared with SHR4640 alone were 0.910 (0.839 to 0.988) and 0.929 (0.893 to 0.966), respectively. Febuxostat had no effect on SHR4640 pharmacokinetic parameters, as the 90%CIs of the geometric mean ratios were all within the range of 0.80 to 1.25. The coadministration of febuxostat and SHR4640 was well tolerated. The coadministration of SHR4640 with febuxostat was not associated with any clinically relevant pharmacokinetic drug interactions. SHR4640 combined with febuxostat had a synergistic effect on reducing uric acid in the pharmacodynamics, with the AUC decreasing from 7440 to 3170 h µmol/L compared with febuxostat alone and from 5730 to 2960 h µmol/L compared with SHR4640 alone. [ABSTRACT FROM AUTHOR]

Capmatinib is a highly specific, potent, and selective mesenchymal-epithelial transition factor inhibitor predominantly eliminated by cytochrome P450 (CYP) 3A4 and aldehyde oxidase. Here, we investigated the effects of a strong CYP3A inhibitor (itraconazole) and a strong CYP3A inducer (rifampicin) on single-dose pharmacokinetics of capmatinib. In addition, serum creatinine and cystatin C were monitored to assess the potential inhibition of renal transporters by capmatinib. This was an open-label, 2-cohort (inhibition and induction), 2-period (capmatinib alone and inhibition/induction periods) study in healthy subjects. In the inhibition cohort, capmatinib (400 mg/day) was given alone, then with itraconazole (200 mg/day for 10 days, 5-day lead-in before coadministration). In the induction cohort, capmatinib (400 mg/day) was given alone, then with rifampicin (600 mg/day for 9 days, 5-day lead-in before coadministration). Fifty-three subjects (inhibition cohort, n = 27; induction cohort, n = 26) were enrolled. Coadministration of itraconazole resulted in an increase of capmatinib area under the plasma concentration-time curve from time 0 to infinity by 42% (geometric mean ratio [GMR], 1.42; 90%CI, 1.33-1.52) with no change in maximum plasma concentration (GMR, 1.03; 90%CI, 0.866-1.22). Coadministration of rifampicin resulted in a reduction of capmatinib area under the plasma concentration-time curve from time 0 to infinity by 66.5% (GMR, 0.335; 90%CI, 0.300-0.374) and a decrease in maximum plasma concentration by 55.9% (GMR, 0.441; 90%CI, 0.387-0.502). After a single dose of capmatinib, a transient increase in serum creatinine was observed with no change in serum cystatin C concentration during the 3-day monitoring period. In conclusion, coadministration of itraconazole or rifampicin resulted in clinically relevant changes in systemic exposure to capmatinib. The transient increase in serum creatinine without any increase in cystatin C suggests inhibition of renal transport by capmatinib. [ABSTRACT FROM AUTHOR]

Combination therapies have become increasingly researched and used in the treatment and management of complex diseases due to their ability to increase the chances for better efficacy and decreased toxicity. To evaluate drug combinations in drug development, pharmacokinetic and pharmacodynamic interactions between drugs in combination can be quantified using mathematical models; however, it can be difficult to deduce which models to use and how to use them to aid in clinical trial simulations to simulate the effect of a drug combination. This review paper aims to provide an overview of the various methods used to evaluate combination drug interaction for use in clinical trial development and a practical guideline on how combination modeling can be used in the settings of clinical trials. [ABSTRACT FROM AUTHOR]

Venetoclax is an approved, orally bioavailable, B‐cell lymphoma type 2 (BCL‐2) inhibitor that is primarily metabolized by cytochrome P450 3A (CYP3A). Polypharmacy is common in patients undergoing treatment for hematological malignancies such as acute myeloid leukemia or chronic lymphocytic leukemia, and although venetoclax exposure has been well characterized with 1 concomitant CYP3A inhibitor, complex drug–drug interactions (DDIs) involving more than 1 inhibitor have not been systematically evaluated. Here, we aimed to describe the potential impact of multiple concomitant CYP3A inhibitors on venetoclax pharmacokinetics (PK) using physiologically based pharmacokinetic (PBPK) and population PK modeling. The modeling approaches were informed by clinical data in the presence of single or multiple CYP3A inhibitors, and the effects of 1 or more inhibitors were systematically considered within these modeling frameworks. The PBPK modeling approach was independently validated against clinical data involving more than 1 CYP3A inhibitor along with CYP3A substrates other than venetoclax. Both approaches indicated that combining a strong CYP3A inhibitor with another competitive CYP3A inhibitor does not seem to result in any additional increase in venetoclax exposure, beyond what would be expected with a strong inhibitor alone. This suggests that the current dose reductions recommended for venetoclax would be appropriate even when 2 or more CYP3A inhibitors are taken concomitantly. However, the results indicate that the involvement of time‐dependent inhibition might lead to additional inhibitory effects over and above the effect of a single strong CYP3A inhibitor. Thus, the clinical management of such interactions must consider the underlying mechanism of the interactions. [ABSTRACT FROM AUTHOR]

Rare diseases are frequently caused by inherited 'monogenic' defects. Treatment interventions that target a specific genetic location or that replaces a specific protein provide rational therapeutic approaches. The current review discusses innovative targeted therapies that act or modulate at the level of DNA, RNA, or protein. They include DNA gene editing, small interference RNA (siRNA), antisense oligonucleotide (ASO), small molecule RNA splicing modifier, and bispecific antibody. With limited numbers of patients, testing multiple dose levels and regimens prior to making an informed dose decision remains one of the major challenges in rare disease drug development. Clinical pharmacology strategically bridges the gap to support drug development and regulatory approvals. Pharmacokinetic drug exposures are driven by absorption, distribution, metabolism, elimination, and in some cases immunogenicity. Drug responses are measured by pharmacodynamic biomarkers that are linked to either short‐ or long‐term clinical outcomes. Understanding the drug exposure–response relationship lies at the heart of bridging the gap that enables a dose decision by balancing effectiveness and safety. Furthermore, and importantly, understanding the influence of intrinsic and extrinsic factors on drug pharmacokinetics enables dose adjustment decisions based on drug exposures. Case examples include the identification of doses and regimens without a formal dose‐finding study, the support of new doses and regimens without conducting additional studies, and the extrapolation of adult drug–drug interaction (DDI) studies to pediatrics without performing a pediatric DDI study. With increasing discoveries of innovative treatment modalities, the responsibility of clinical pharmacologists is expected to grow and enhance the development of novel treatments for rare diseases. [ABSTRACT FROM AUTHOR]

There are more than 7000 rare diseases affecting approximately 30 million people in the United States. More than 90% of these diseases lack approved therapies. Several challenges face the development of "orphan drugs", such as the small populations of patients, high development costs, and long development timelines. This study evaluates clinical pharmacology assessments conducted during the development of drugs to treat rare diseases approved by the United States Food and Drug Administration in 2020 and 2021. Thirty‐nine new drug applications (NDAs) have been identified and the associated regulatory reviews, approved labels, and approval letters were reviewed. Approximately, 95%, 74%, and 77% of these submissions contained at least one type of drug–drug interaction, the effect of organ impairment (hepatic and renal) on drug exposure, and QT liability assessment, respectively. Modeling and simulation approaches were utilized to address many clinical pharmacology questions, with population pharmacokinetic analyses used extensively in the evaluation of the effect of organ impairment on drug exposure and with physiologically based pharmacokinetic analyses used mainly in assessing drug interaction risks. In general, the clinical pharmacology packages in the NDAs of orphan drugs are not optimal and more work is needed to obtain a complete clinical pharmacology package at the time of initial approval to ensure the safe and effective use of these drugs across the spectrum of the target patient population. This study provides insights into the clinical pharmacology studies needed for drugs to treat rare diseases and would help both the regulators and drug developers to identify challenges and opportunities in conducting clinical pharmacology assessments for drugs developed to treat rare diseases. [ABSTRACT FROM AUTHOR]

Comprehensive characterization of transporter mediated drug‐drug interactions (DDIs) is important to formulate clinical management strategies and ensure the safe and effective use of concomitantly administered drugs. The potential of a drug to inhibit transporters is predicted by comparing the ratio of the relevant concentration (depending on the transporter) and the half maximum inhibitory concentration to a predefined "cutoff" value. If the ratio is greater than the cutoff value, modeling approaches such as physiologically based pharmacokinetic modeling or a clinical DDI trial may be recommended. Because false‐positive (in vitro data suggest the potential for a DDI, whereas no significant DDI is observed in vivo) and false‐negative (in vitro data does not suggest the potential for a DDI, whereas significant DDI is observed in vivo) outcomes have been observed, there is interest in exploring additional approaches to facilitate prediction of transporter‐mediated DDIs. The idea of assessing changes in the concentration of endogenous biomarkers (which are substrates of clinically relevant transporters) to gain insight on the potential for a drug to inhibit transporter activity has received widespread attention. This brief report describes how endogenous biomarkers may help to expand the DDI assessment toolkit, highlights some current knowledge gaps, and outlines a conceptual framework that may complement the current paradigm of predicting the potential for transporter‐mediated DDIs. [ABSTRACT FROM AUTHOR]

Tacrolimus and voriconazole are usually used simultaneously in lung transplantations. Voriconazole can increase tacrolimus concentrations by inhibiting the cytochrome P450 (CYP) enzyme, which poses a great challenge for dose adjustment. The aim of this study is to clarify the correlation between voriconazole exposure and tacrolimus trough concentrations (C0), and to establish a population pharmacokinetic model including voriconazole trough concentrations (VOZ) as a covariate for dose optimization. All data were retrospectively collected from lung transplantation patients who were subjected to therapeutic drug monitoring of tacrolimus and voriconazole. The correlation between C0 and VOZ or voriconazole daily doses was analyzed by Spearman's correlation. A total of 52 patients accounting for 351 pairs of tacrolimus and voriconazole trough concentrations were included. C0 and C0/daily dose of tacrolimus (DD) had a significant correlation with VOZ (P <.01) rather than voriconazole daily doses. A linear one‐compartment model with first‐order absorption and elimination was used as the basic model for population pharmacokinetic analysis. Body weight (WT), DD, VOZ, and hematocrit (HCT) were included as covariates in the final model. With the increase in voriconazole concentrations, the apparent total clearance (clearance/bioavailability, CL/F) of tacrolimus decreased significantly. The simulation results showed that the highest proportion of C0 within the target range can only reach <50% when the optimal initial drug regimen was given. Therefore, both tacrolimus and voriconazole concentrations need to be continuously monitored during treatments in lung transplantation patients, and the tacrolimus dose can be optimized according to VOZ based on the established pharmacokinetic model. [ABSTRACT FROM AUTHOR]

Artemisinin is an antimalarial compound derived from the plant Artemisia annua L., also known as sweet wormwood. According to the World Health Organization, artemisinin‐based combination therapy (ACT) is an essential treatment for malaria, specifically Plasmodium falciparum, which accounts for most malaria‐related mortality. ACTs used to treat uncomplicated malaria include artemether‐lumefantrine, artesunate‐amodiaquine, artesunate‐mefloquine, artesunate‐sulphadoxine‐pyrimethamine, and dihydroartemisinin‐piperaquine. Although the mechanism of action and clinical capabilities of artemisinin in malaria treatment are widely known, more information on the potential for drug interactions needs to be further investigated. Some studies show pharmacokinetic and pharmacodynamic drug interactions with HIV antiviral treatment but few studies have been conducted on most other drug classes. Based on known genotypes of cytochrome P450 (CYP) enzymes, CYP2B6 and CYP3A are primarily involved in the metabolism of artemisinin and its derivatives. Reduced functions in these enzymes can lead to subtherapeutic concentrations of the active metabolite, dihydroartemisinin, that may cause treatment failure, which has been shown in some studies with cardiovascular, antibiotic, and antiparasitic drugs. Although the clinical importance remains unclear to date, clinicians should be aware of potential drug‐drug interactions and monitor patients on ACT closely. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection, which causes coronavirus disease 2019 (COVID‐19), manifests as mild respiratory symptoms to severe respiratory failure and is associated with inflammation and other physiological changes. Of note, substantial increases in plasma concentrations of α1‐acid‐glycoprotein and interleukin‐6 have been observed among patients admitted to the hospital with advanced SARS‐CoV‐2 infection. A physiologically based pharmacokinetic (PBPK) approach is a useful tool to evaluate and predict disease‐related changes on drug pharmacokinetics. A PBPK model of imatinib has previously been developed and verified in healthy people and patients with cancer. In this study, the PBPK model of imatinib was successfully extrapolated to patients with SARS‐CoV‐2 infection by accounting for disease‐related changes in plasma α1‐acid‐glycoprotein concentrations and the potential drug interaction between imatinib and dexamethasone. The model demonstrated a good predictive performance in describing total and unbound imatinib concentrations in patients with SARS‐CoV‐2 infection. PBPK simulations highlight that an equivalent dose of imatinib may lead to substantially higher total drug concentrations in patients with SARS‐CoV‐2 infection compared to that in patients with cancer, while the unbound concentrations remain comparable between the 2 patient populations. This supports the notion that unbound trough concentration is a better exposure metric for dose adjustment of imatinib in patients with SARS‐CoV‐2 infection, compared to the corresponding total drug concentration. Potential strategies for refinement and generalization of the PBPK modeling approach in the patient population with SARS‐CoV‐2 are also provided in this article, which could be used to guide study design and inform dose adjustment in the future. [ABSTRACT FROM AUTHOR]

Irinotecan‐induced hepatotoxicity can cause severe clinical complications in patients; however, the underlying mechanism and factors affecting hepatotoxicity have rarely been investigated. In this cross‐sectional study, we screened all clinical, demographic, medication, and genetic variables among 126 patients receiving irinotecan and explored potential associations with the incidence and time to onset of irinotecan‐induced hepatotoxicity. Approximately 38.9% of the patients suffered from hepatotoxicity after irinotecan administration. The presence of cardiovascular diseases increases the incidence of hepatotoxicity ≈2.9‐fold and doubles the hazard of time to hepatotoxicity. Patients with liver metastasis had a >4‐fold higher risk of hepatotoxicity and a 3.5‐fold increased hazard of time to hepatotoxicity compared to those without liver metastasis. Patients who took cytochrome P450 (CYP) 3A inducers had a 4.4‐fold increased incidence of hepatotoxicity, and furthermore, concomitant use of platinum‐based antineoplastics revealed 4.2 times the hazard of time to hepatotoxicity compared to those receiving antimetabolites. The cumulative dose of irinotecan (5‐9 cycles) increased hepatotoxicity by 8.5 times. However, the genotypes and phenotypes of UGT1A1*28/*6 failed to be predictive factors of hepatotoxicity. The findings of this study suggest that irinotecan‐induced hepatotoxicity is not directly associated with genetic variables but is mostly related to concomitant use of CYP3A4 inducers and platinum, as well as the presence of liver metastasis and cardiovascular disease. Thus, close monitoring of liver function is recommended, especially in patients with liver impairment or using CYP3A inducers and platinum antineoplastic drugs, which may be the best way to prevent hepatotoxicity. [ABSTRACT FROM AUTHOR]

Inclisiran increases the number of low‐density lipoprotein surface receptors expressed on hepatocytes using small interfering RNA directed against proprotein convertase subtilisin/kexin type 9 (PCSK9) mRNA. This novel mechanism can reduce low‐density lipoprotein by ≈50%, like reductions achieved with high‐intensity statins or PCSK9‐inhibiting monoclonal antibodies. Inclisiran is indicated in the treatment of adults with heterozygous familial hypercholesterolemia or clinical atherosclerotic cardiovascular disease, unable to achieve target LDL concentrations with diet and maximally tolerated statin therapy. It is more expensive than PCSK9‐inhibiting monoclonal antibodies and still lacks clinical evidence for the expected reduction in atherosclerotic cardiovascular disease events when added to statin therapy. Although some patients experience injection‐site reactions of mild or moderate severity, current evidence suggests a strong safety profile with total and serious adverse events approximating that of placebo. The dosing protocol of inclisiran offers its biggest clinical advantage over PCSK9‐inhibiting monoclonal antibodies, being administered only every 6 months after initial baseline and 3‐month doses rather than every second or fourth week. Unlike PCSK9‐inhibiting monoclonal antibodies, inclisiran has not as yet been noted to induce neutralizing antidrug antibodies that can impact drug efficacy. Thus, inclisirin is a novel small interfering RNA molecule that provides further options in the management of hypercholesterolemia refractory to statins alone. However, cost and evidence considerations suggest it should not supplant adjunctive therapy with the PCSK9‐inhibiting monoclonal antibodies, despite having an efficacy, safety, tolerability, and drug interaction profile superior to other antihypercholesterolemic options such as lomitapide, niacin, bile acid sequestrants, and bempedoic acid. [ABSTRACT FROM AUTHOR]

The use of polypharmacy has become significantly more common over the past two decades, increasing the risk of drug–drug interactions and adverse drug reactions. Pharmacogenomic (PGx) assays have the purported benefit of being able to predict an individual's response to a specific medication based on genetic markers, which may facilitate the development of optimized medication regimens for patients prescribed polypharmacy. This 12‐week pilot study examined the impact of the PGx results on the clinical management of Veterans who were prescribed psychiatric polypharmacy. Psychiatric medication providers were given access to the PGx assay results, including notification of drug–drug–gene interactions computed from an algorithm decision tool, to assist with medication management decisions. Veteran outpatients (N = 53) prescribed polypharmacy (mean = 13.15 medications) were enrolled into the study. In 92.4% of cases, providers changed medications at baseline, with 83% of providers indicating that they changed their original medication plan based on the PGx results. Clinical improvement over the 12‐week treatment phase was seen in depression (F(1.63, 45) = 5.45, P =.01, η2 =.11) and mental health quality of life (F(2.00, 45) = 4.16, P <.05, η2 =.16). Adverse drug effects were unchanged or improved over time. Rates of polypharmacy remained unchanged. The results suggest that medication changes based on the PGx assay may be beneficial in a complex patient population prescribed polypharmacy. [ABSTRACT FROM AUTHOR]

Despite the advancement of antiretroviral therapy (ART) for the treatment of human immunodeficiency virus (HIV), drug–drug interactions (DDIs) remain a relevant clinical issue for people living with HIV receiving ART. Antiretroviral (ARV) drugs can be victims and perpetrators of DDIs, and a detailed investigation during drug discovery and development is required to determine whether dose adjustments are necessary or coadministrations are contraindicated. Maintaining therapeutic ARV plasma concentrations is essential for successful ART, and changes resulting from potential DDIs could lead to toxicity, treatment failure, or the emergence of ARV‐resistant HIV. The challenges surrounding DDI management are complex in special populations of people living with HIV, and often lack evidence‐based guidance as a result of their underrepresentation in clinical investigations. Specifically, the prevalence of hepatic and renal impairment in people living with HIV are between five and 10 times greater than in people who are HIV‐negative, with each condition constituting approximately 15% of non‐AIDS‐related mortality. Therapeutic strategies tend to revolve around the treatment of risk factors that lead to hepatic and renal impairment, such as hepatitis C, hepatitis B, hypertension, hyperlipidemia, and diabetes. These strategies result in a diverse range of potential DDIs with ART. The purpose of this review was 2‐fold. First, to summarize current pharmacokinetic DDIs and their mechanisms between ARVs and co‐medications used for the prevention and treatment of hepatic and renal impairment in people living with HIV. Second, to identify existing knowledge gaps surrounding DDIs related to these special populations and suggest areas and techniques to focus upon in future research efforts. [ABSTRACT FROM AUTHOR]

The article discusses emphasis in the field of clinical pharmacology has been placed on pharmacometrics and systems biology and the mathematical modeling of complex biologic processes. Topics include applied to clinical trial simulations, prediction of drug– drug interactions using physiological pharmacokinetic models; and appropriate to revisit certain basic principles of clinical pharmacology that are relevant to pharmacotherapy.

Patients admitted to the intensive care unit (ICU) are frequently exposed to potential drug‐drug interactions (pDDIs). However, reported frequencies of pDDIs in the ICU vary widely between studies. This can be partly explained by significant variation in their methodological approach. Insight into methodological choices affecting pDDI frequency would allow for improved comparison and synthesis of reported pDDI frequencies. This study aimed to evaluate the association between methodological choices and pDDI frequency and formulate reporting recommendations for pDDI frequency studies in the ICU. The MEDLINE database was searched to identify papers reporting pDDI frequency in ICU patients. For each paper, the pDDI frequency and methodological choices such as pDDI definition and pDDI knowledge base were extracted, and the risk of bias was assessed. Each paper was categorized as reporting a low, medium, or high pDDI frequency. We sought associations between methodological choices and pDDI frequency group. Based on this comparison, reporting recommendations were formulated. Analysis of methodological choices showed significant heterogeneity between studies, and 65% of the studies had a medium to high risk of bias. High risk of bias, small sample size, and use of drug prescriptions instead of administrations were related to a higher pDDI frequency. The findings of this review may support researchers in designing a reliable methodology assessing pDDI frequency in ICU patients. The reporting recommendations may contribute to standardization, comparison, and synthesis of pDDI frequency studies, ultimately improving knowledge about pDDIs in and outside the ICU setting. [ABSTRACT FROM AUTHOR]

The therapeutic efficacy of clopidogrel as an antiplatelet drug varies among individuals, being the mainstream hypothesis that its bioavailability depends on the individual genetic background and/or interactions with other drugs. A total of 477 patients receiving double antiaggregation therapy with aspirin and clopidogrel, after suffering a first event, were followed for 1 year to record relapse, as a surrogate end point to measure their therapeutic response, as defined by presenting with an acute coronary event (unstable angina, ST‐segment–elevation myocardial infarction, or non–ST‐segment–elevation myocardial infarction), stent thrombosis/restenosis, or cardiac mortality. Anthropometric, clinical, and pharmacological variables along with CYP2C19 genotypes were analyzed for their association with the disease relapse phenotype. Only 75 patients (15%) suffered a relapse, which occurred during the first 6 months of therapy, with a peak at 4.5 months. An initial univariate analysis identified that patients in the relapse group were significantly older (67.4 ± 11.0 vs 61.6 ± 12.3 years old) and presented with diffuse coronary disease, insulin‐dependent type 2 diabetes mellitus dyslipidemia, and arterial hypertension. A poor clinical response to the platelet antiaggregation regime also occurred more frequently among patients taking acenocoumarol and calcium channel blockers, along with aspirin and clopidogrel, while no association was found according to CYP2C19 genotypes. A retrospective multivariate analysis indicated that patients belonging to the nonresponder phenotype to treatment with aspirin and clopidogrel were older, presented with diffuse coronary disease, a group largely overlapping with type 2 insulin‐dependent diabetes mellitus, and were taking dihidropyrimidinic calcium channel blockers. [ABSTRACT FROM AUTHOR]

Concomitant nonsteroidal anti‐inflammatory drug (NSAIDs) and antithrombotic drug use is associated with an increased risk of bleeding, mainly gastrointestinal. The goal of this study was to quantify the transient increase in the risk of hospitalization for bleeding associated with NSAID use in patients treated with antiplatelet agents or anticoagulants. We performed a unidirectional case‐crossover study using the EGB (Échantillon généraliste de bénéficiaires), a permanent random sample of the French nationwide health database. Patients receiving antithrombotic therapy and hospitalized for bleeding between 2009 and 2017 were included. We compared their NSAID exposure during a 15‐day hazard window immediately before hospital admission to 3 earlier 15‐day control windows. The risk of hospitalization for bleeding associated with the recent use of NSAIDs was estimated using conditional logistic regression to estimate odds ratios (ORs). During the study period, 33 patients treated with anticoagulants and 253 treated with antiplatelet agents received NSAIDs and were included in the case‐crossover analysis. We found an increased risk of hospitalization for gastrointestinal bleeding after exposure to NSAIDs, with an adjusted OR of 3.59 (95%CI, 1.58‐8.17) in patients receiving anticoagulant therapy and 1.44 (95%CI, 1.07‐1.94) in patients receiving antiplatelet therapy. The risk of nongastrointestinal bleeding was also increased after exposure to NSAIDs with an adjusted OR of 2.72 (95%CI, 1.23‐6.04) in patients exposed to anticoagulant therapy. The risk of gastrointestinal and nongastrointestinal bleeding increases after NSAID use in patients treated with anticoagulants, while the risk of gastrointestinal bleeding increases, but to a lesser extent in those treated with antiplatelets. [ABSTRACT FROM AUTHOR]

As a first‐in‐class, selective, potent inhibitor of the isocitrate dehydrogenase‐2 (IDH2) mutant protein, enasidenib was approved by the US Food and Drug Administration in 2017 for the treatment of adult patients with relapsed or refractory acute myeloid leukemia with an isocitrate dehydrogenase‐2 mutation. An in vitro study showed that enasidenib at clinically relevant concentrations has effects on multiple drug metabolic enzymes and transporters, including inhibition of P‐glycoprotein, breast cancer resistance protein, organic anion transporter (OAT) P1B1, and OATP1B3 transporters. Therefore, a drug‐drug interaction study was conducted to assess the impact of enasidenib at steady state on the pharmacokinetics of several probe compounds in patients with relapsed or refractory acute myeloid leukemia or myelodysplastic syndrome, including the probes herein described in this article, digoxin and rosuvastatin. Results from 8 patients (all Asian) with a mean age of 67.1 years showed that following coadministration of enasidenib (100 mg, 28‐day once‐daily schedule) for 28 days (at steady state), digoxin's (0.25 mg) area under the plasma concentration–time curve from time 0 to 30 days was 1.2‐fold (90% confidence interval, 0.9‐1.6), compared with digoxin alone. Following coadministration of enasidenib (100 mg, 28‐day once‐daily schedule) for 28 days (at steady state), rosuvastatin's (10 mg) area under the plasma concentration–time curve from time 0 to infinity was 3.4‐fold (90% confidence interval, 2.6‐4.5) compared with rosuvastatin alone. These results should serve as the basis for dose recommendations for drugs that are substrates of P‐glycoprotein, breast cancer resistance protein, OATP1B1, and OATP1B3 transporters, when used concomitantly with enasidenib. [ABSTRACT FROM AUTHOR]

Oritavancin and dalbavancin are long‐acting lipoglycopeptides with activity against susceptible gram‐positive bacteria, including methicillin‐resistant Staphylococcus aureus. Though similar in structure to traditional glycopeptide antibiotics like vancomycin, these antibiotics have terminal half‐lives >10 days, and, as a result, there is potential for administration of vancomycin to a patient while oritavancin or dalbavancin are still appreciably present in serum. Given the structural similarities, this creates an opportunity for lab assay interference when performing therapeutic drug monitoring for vancomycin. Following higher‐than‐expected serum vancomycin concentrations in a patient who received both oritavancin and vancomycin within a short time frame, we evaluated the potential for lipoglycopeptide interference with clinical vancomycin assays. Five platforms covering 3 immunoassay technologies were used to quantify vancomycin concentrations in serum spiked with oritavancin or dalbavancin. Oritavancin generated spurious vancomycin concentrations (20%‐84% increase) in both enzyme‐multiplied immunoassay technique and a particle‐enhanced turbidimetric inhibition immunoassay. However, the improper detection of oritavancin was not consistent across all particle‐enhanced turbidimetric inhibition immunoassay platforms. Dalbavancin interference was not detected on any of the platforms tested. The interference from oritavancin may result in falsely elevated vancomycin concentrations and, subsequently, inappropriately adjusted vancomycin doses. [ABSTRACT FROM AUTHOR]