The convergence of MIDD and AI represents a natural evolution from established quantitative frameworks toward a broader, more powerful computational toolbox that can address persistent MIDD challenges such as high-dimensional data integration, complex biology, and operational constraints in evidence generation. AI can both enhance traditional MIDD through hybrid approaches and extend MIDD's horizon to include various AI models. Recent publications of guidance demonstrate the agency's preparation on the acceptance of AI in MIDD.
Despite integration of artificial intelligence (AI) into drug discovery and an expanding global medicines pipeline, new drug approvals have declined, highlighting a paradox in biopharma: More drugs discovered, but less approvals, higher costs, and longer timelines. Reasons for the decreased research and development (R&D) efficiency are multifactorial, in part driven by the complexity of new modalities, difficult targets and indications, and persistence of cognitive biases in clinical decision-making. One proposed solution to address R&D productivity challenges includes the adoption of organization-wide quantitative decision frameworks (QDFs). QDFs have the potential to increase R&D productivity by integrating quantitative assessments of program risk and value, clinical development costs, time, and probability of success into product valuations. A QDF integrates emerging clinical characteristics of the product through model-predicted efficacy and safety and links them to common valuation models to quantify the impact of product risk and uncertainty on value at different development stages. Context-aware AI can dynamically incorporate relevant unstructured information including clinical, competitor, market, and regulatory data into a QDF. The framework may be applied to compare clinical development scenarios for a single program, evaluate trade-offs between programs, and support portfolio-level decision making. Application of comprehensive QDFs in drug development promotes organizational alignment and transparency in product valuations, thereby supporting rationale decision-making, investment partnership negotiations, and product reimbursement assessment.
Wet age-related macular degeneration (wAMD) is a leading cause of vision loss, and human dose selection for ocular therapeutics remains a key translational challenge. We developed a physiologically based pharmacokinetic (PBPK) model for EB-203 to guide dose selection by translating preclinical ocular tissue exposure to human. PK data were obtained from mouse and rabbit studies and human plasma PK from a phase I trial. The model incorporated species-specific ocular physiology, drug permeability, and pharmacokinetics, and was implemented using an open-source PBPK modeling software (MoBi, version 12.2, Open Systems Pharmacology). Drug-specific model parameters were estimated using mouse and rabbit plasma and ocular tissue concentration data. Human plasma PK data from a Phase I clinical trial were then used to estimate human-specific systemic clearance, and human ocular tissue concentrations were subsequently predicted by the model. Simulated human retinal drug concentrations (AUC, Cmax, and Cavr) under multiple dosing regimens were evaluated against preclinical efficacy thresholds to determine the optimal dosing strategy. The model adequately described drug disposition across species, with most predicted PK parameters falling within a 2-fold error of the observed data. Simulations suggested that a 4% four-times-daily dosing regimen in humans achieves retinal exposure comparable to the efficacious mouse concentrations. No significant systemic accumulation was observed, indicating a favorable safety profile. This PBPK framework provides a practical approach for predicting human ocular drug exposure and can be adapted to other compounds or ocular indications in the absence of direct tissue sampling. These results support further clinical evaluation of EB-203 for the treatment of wAMD.
Early clinical development of anticancer drugs traditionally focused on exploring candidate treatments in patients. For instance, due to their cytotoxic action, administration of chemotherapies to healthy volunteers (HVs) for evaluation of their pharmacokinetic profiles was not considered feasible nor ethical from a safety and tolerability perspective. With the introduction of more targeted anticancer agents, such as the tyrosine kinase inhibitors (TKIs), enrolment of HVs in clinical pharmacology trials has become an advantageous alternative to early, non-therapeutic testing in cancer patients. Futibatinib (Lytgobi) is a TKI acting upon the fibroblast growth factor receptor (FGFR) 1-4 and is approved for the treatment of patients with locally advanced or metastatic intrahepatic cholangiocarcinoma harboring FGFR2 gene fusions or other rearrangement. During its clinical development, a series of studies were conducted enrolling HVs, for example, to evaluate pharmacokinetic properties (ADME), drug-drug interaction (DDI) potential and QT liability. In this review article, we will discuss when and why enrolment of HVs can be considered for small molecule anticancer drug trials and touch upon the risks and limitations as well as the advantages of administering this type of drugs to HVs. The case of futibatinib provides an illustrative example of the opportunities and challenges for exploring an anticancer small molecule in HVs.
Pairwise drug-drug interaction databases flag co-prescribed pairs, but they under-weight multi-drug combinations that drive adverse drug events in older adults. We studied that gap in Virginia All-Payer Claims Database records (2016-2019) for adults aged 65-114 years with non-opioid emergency department visits (n = 1182 cases; 16,105 matched controls across three geriatric age bands). Features came from pharmacy and medical claims in a short pre-index window optimized for acute ADE timing (21-30 days by age band; 21 days for ages 65-84). We trained gradient-boosting models separately among patients with similar pre-index healthcare contact volume (2016-2018 training; 2019 holdout), then used Formal Feature Attribution to score drug pairs and triplets and Intervention Rate ranks to order deprescribing review. On the 2019 holdout, geriatric AUPRC was 0.101-0.335 (PR lift 1.6×-4.2×). FFA flagged 115 synergistic pairs and 312 high-confidence triplets (e.g., furosemide + hydrochlorothiazide + lisinopril; digoxin + furosemide + amiodarone, IE = 8.7). Top Intervention Rate drugs included simvastatin, furosemide, and alprazolam. Moderate preventive Z-code monitoring (Q2) was protective (OR = 0.25; 95% CI 0.18-0.34) versus no monitoring, while fragmented high-intensity monitoring (Q4) was not. The framework prioritizes medication combinations for pharmacist review in claims data; it does not replace pharmacokinetic confirmation or prove that changing a drug caused fewer ED visits.
Rocbrutinib is a fourth-generation Bruton's tyrosine kinase (BTK) inhibitor that covalently binds wild-type BTK and non-covalently engages the C481S mutant. Its pharmacokinetic (PK) characteristics in healthy Chinese subjects remain unclear. This study aimed to support maximum recommended starting dose (MRSD) selection and predict exposure of rocbrutinib in healthy Chinese subjects via an integrated model-informed strategy. We determined key extrapolation parameters and preclinical PK in CD-1 mice and beagle dogs. Three approaches were employed to determine the MRSD: The no observed adverse effect level (NOAEL) dose method based on body surface area, the NOAEL exposure method based on a physiologically based pharmacokinetic (PBPK) model, and the minimum effective dose method based on a PBPK model. The PBPK model was developed and validated using preclinical and clinical PK data. Predicted MRSD values by the NOAEL dose method, the NOAEL exposure method, and the minimum effective dose method were 48.7, 9.8, and 10.5 mg, respectively. Considering the lowest predicted value and available tablet strength, a starting dose of 12.5 mg was selected. Predicted plasma concentration-time profiles were consistent with those observed in animals and humans. The fold error for Cmax and AUC fell within the 0.5-2.0 range. The model further predicted brain tissue exposure across species. Moreover, the predicted brain BTK occupancy at 12.5 mg was approximately 37% for wild-type BTK and 6% for C481S mutant BTK. The PBPK model serves as a valuable tool for dose selection, PK prediction, and CNS target engagement evaluation, supporting the clinical development of rocbrutinib.
Despite the lack of evidence of a mechanism-based drug interaction, some professional organizations have advised caution coadministering apixaban and levetiracetam. Eligible patients taking apixaban with or without levetiracetam for any indication for at least 72 h were screened from the electronic medical records. Plasma samples from 77 patients were retrieved from salvaged clinical blood collections. Apixaban minimum and maximum concentrations (Cmin and Cmax) were compared using descriptive statistics in R. While the median Cmin and Cmax appear to be marginally elevated, there were no apparent differences relative to apixaban alone. These findings suggest no apparent interaction of levetiracetam and support the current lack of labeled warning of concomitant apixaban dosing.
Numerous pharmacogenomic (PGx) associations of pharmacokinetics and pharmacodynamics of commonly prescribed medications have been reported. Yet, the evidence supporting their association on survival remains underexamined. We sought to investigate the CYP2D6-metoprolol association and determine whether the established variability in drug exposure and hemodynamic response would translate into differences in mortality across metabolizer status in the Montreal Heart Institute Hospital Cohort. Single random plasma samples were collected from 996 patients receiving metoprolol tartrate. Bioanalytical quantification was performed via liquid chromatography-tandem mass spectrometry, while CYP2D6 metabolizer status was based on standardized classifications. Cox regression models adjusted for age, sex, cardiovascular history, concomitant medications, and CYP2D6 inhibitors were used to assess the time to death since enrolment. Overall, 24.3% (n = 242) of patients were deceased at follow-up (median 101.4 months). Higher metabolizer status at CYP2D6 was associated with lower risk of death (HR = 0.82, 95% CI: 0.67-0.99; p = 0.04). Metoprolol concentrations were no longer associated with mortality after adjusting for possible confounders. Our study suggests that CYP2D6-inferred metabolizer status can inform on mortality risk in patients treated with metoprolol. Larger initiatives are required to confirm the association of CYP2D6 metabolizer status with clinical events beyond pharmacokinetic and hemodynamic considerations.
Timely development and approval of innovative drugs are essential for patient access and public health. China has implemented regulatory reforms, including priority review and conditional approval, to accelerate drug development. We conducted a cross-sectional study of 232 innovative drugs, comprising chemical drugs, biologics, and traditional Chinese medicines (TCMs), approved by the National Medical Products Administration during 2010-2025. Development timelines for 227 drugs were analyzed using generalized linear models with a gamma distribution and log link to assess associations with drug type, regulatory pathways, orphan drug status, reform stage, indication, primary endpoint duration, and sample size. Median total development time was 6.94 years (IQR, 4.85-9.97), with biologics having the shortest duration (5.14 years) and TCMs the longest (10.22 years). Drug type and conditional approval were primary variables associated with total development time. Conditional approval was associated with shorter total development time among oncology drugs (relative mean ratio [RMR] = 0.66, p < 0.001), with a significant interaction by indication (RMR = 2.08, p < 0.001). Among drugs not receiving conditional approval, clinical development time was longer in later reform stages by up to 52%, whereas marketing application review time decreased in later reform stages overall, with a 64% reduction in Stage III (RMR = 0.36, p < 0.001). Priority review and conditional approval were associated with 26% and 28% reductions in review time, respectively (both p < 0.05). These findings suggest heterogeneous associations of China's regulatory reforms across pathways and therapeutic areas, consistent with differential contributions of administrative acceleration and evidentiary modification to development timelines.
ABSTRACT An increasing number of novel molecular entities and biosimilars reach the European Medicines Agency (EMA) supported by pivotal evidence derived predominantly from Asian patient populations. This creates regulatory challenges that the existing ICH E5 framework was not originally designed to address. We performed a retrospective regulatory document analysis of 10 EMA applications assessed between 2021 and 2025 in which pivotal evidence was predominantly Asian. European Public Assessment Reports were reviewed for bridging strategy, intrinsic ethnic sensitivity, data‐integrity findings, clinical‐practice comparability, and regulatory outcome. Eight applications were authorized and two were withdrawn. Reflecting the eligible EMA case set during 2021–2025, the included products were concentrated in oncology, particularly PD‐1/PD‐L1 monoclonal antibodies. EMA decisions were shaped by four recurring considerations: expected intrinsic ethnic sensitivity, adequacy of bridging evidence, Good Clinical Practice or manufacturing findings, and comparability of Chinese clinical practice with EU standard of care. Clinical practice divergence was the least standardized source of residual uncertainty. Asian‐dominant evidence can support EU authorization, but transparent assessment of extrinsic practice differences is essential for reliable translation.
ABSTRACT Omics biomarkers comprise several molecular entities, including genomic, epigenomic, transcriptomic, proteomic, and metabolomic signatures. They play an essential role in oncology drug development and clinical settings by enabling personalized medicine. In precision oncology, validating biomarkers is a greater challenge than discovering them. The biomarkers discussed here span a wide evidentiary spectrum, from those endorsed in clinical guidelines to those that remain investigational.
ABSTRACT The exclusion of pregnant individuals from clinical trials has resulted in a significant medical knowledge gap for this population. Leveraging real‐world data (RWD) provides a critical window into the prevalence and use of medications during pregnancy. Using the health plan claims database IQVIA PharMetrics(R) Plus, we evaluated the use of disease‐modifying therapies (DMTs) and concomitant medications in pregnant individuals. We focused on two case examples—Crohn's Disease (CD) and Multiple Sclerosis (MS)—analyzing patients with a history of DMT use before, during, and after pregnancy between 2021 and 2024. In the CD cohort, we found that 47% (n = 1187) of individuals used a DMT during pregnancy. Among these patients, 67% (n = 694) were prescribed a monoclonal antibody (e.g., adalimumab, ustekinumab). In the MS cohort, 27% (n = 300) of individuals used a DMT during pregnancy, of which the predominant medication used was glatiramer acetate, a well‐established immunomodulator. Across both cohorts, a targeted concomitant medication analysis focusing on major metabolic and efflux pathways revealed that several medications taken during pregnancy interact with CYP3A4, P‐gp, or BCRP. Other potential DDI pathways were not assessed. These RWD analyses confirm that pregnant individuals are often exposed to complex medication regimens, highlighting the need to understand drug use and disposition in this vulnerable population.
ABSTRACT Malaria remains a significant global health burden, particularly among pediatric populations in endemic regions. Recent advances in preventive interventions highlight the complementary roles of vaccines and long‐acting monoclonal antibodies (mAbs) in reducing infection risk. Vaccines such as RTS,S/AS01 and R21/Matrix‐M induce active immunity through polyclonal antibody and CD4 + T‐cell responses against the circumsporozoite protein (CSP), providing durable, population‐level protection. In contrast, mAbs including CIS43LS and L9LS confer immediate, passive immunity, with protection directly linked to systemic exposure and duration above protective thresholds. Translational pharmacokinetic/pharmacodynamic (PK/PD) modeling and physiologically based pharmacokinetic (PBPK) approaches are central to optimizing these interventions. Population PK models, combined with controlled human malaria infection (CHMI) studies, enable model‐informed selection of dose, route, and timing to achieve efficacious exposures, while PBPK models facilitate extrapolation to pediatric and special populations by incorporating developmental physiology, protein turnover, and FcRn‐mediated recycling. Clinical studies demonstrate robust protection from both vaccines and mAbs, supporting complementary combination strategies. Forward‐looking approaches integrate vaccines and long‐acting mAbs to leverage broad, durable immunity from vaccines and immediate, predictable protection from antibodies, particularly during seasonal peaks or in high‐risk groups. This review emphasizes how quantitative PK/PD and PBPK modeling provide a framework to optimize dosing, predict durability of protection, and guide clinical development across diverse populations. These strategies underscore the value of model‐informed translational pharmacology in accelerating development of next‐generation malaria preventive interventions against malaria.
ABSTRACT Pharmacogenomics has the potential to improve medication responses for the public; however, its adoption in clinical practice has been slow. Consumer representation and engagement are crucial for pharmacogenomics implementation, since it is their data that is central to testing. This study investigated the Australian public's awareness, knowledge, and perceptions of pharmacogenomics via a mixed‐methods approach. Firstly, a cross‐sectional survey with a nationally representative sample of 772 members of the Australian general public assessed the frequency of pharmacogenomics awareness, knowledge, perceived benefit and concern. Subsequent semi‐structured interviews with 21 members of the Australian public provided deeper insights into perceptions through thematic analysis. Over three‐quarters (76.3%) of participants were unaware of pharmacogenomics, while 6.2% categorized themselves as aware and knowledgeable about the topic. Interviews found five major themes: (1) Knowledge and Understanding, (2) Trust, (3) Personalized and Holistic Care, (4) Clinical Utility, and (5) Accessibility. Overall, participants highlighted several key steps for pharmacogenomics integration into clinical practice and to increase awareness. These include the importance of simplifying the terminology, providing education and transparency to overcome apprehension, addressing cost concerns, communicating the value clearly and increasing community engagement.
ABSTRACT Imlunestrant is a next‐generation, oral, selective estrogen receptor degrader approved for treating adults with estrogen receptor‐positive, human epidermal growth factor receptor 2‐negative, ESR1‐mutated advanced/metastatic breast cancer. Imlunestrant is metabolized partly by cytochrome P450 (CYP)3A4 oxidation and conjugation with glucuronic acid and sulfate; thus, pharmacokinetic and drug–drug interaction (DDI) studies for treatments utilizing similar pathways are essential for clinical development. Three open‐label, phase 1 studies enrolled 182 healthy females of nonchildbearing potential to assess the tolerability, safety, and pharmacokinetics of imlunestrant as monotherapy or coadministered with other drugs. Drug concentrations were quantified by validated liquid chromatography–mass spectrometry. Participants tolerated imlunestrant at 200–800‐mg doses, alone or with a CYP3A inhibitor (itraconazole), CYP3A inducer (carbamazepine), P‐glycoprotein (P‐gp) inhibitor (quinidine), and substrates of CYP2C8 (repaglinide), CYP2C19 (omeprazole), CYP2D6 (dextromethorphan), CYP3A (midazolam), P‐gp (digoxin), and BCRP (rosuvastatin). Frequent treatment‐related adverse events included headache (range, 4%–20%), diarrhea (2%–20%), and constipation (0%–16%). In the presence of imlunestrant, exposures (AUC0–∞) increased for dextromethorphan 1.33‐fold (90% CI, 1.22–1.46), digoxin 1.39‐fold (1.22–1.59), and rosuvastatin 1.49‐fold (1.14–1.97). Imlunestrant exposure (AUC0–∞) decreased in combination with quinidine (CYP2D6 inhibitor) 0.87‐fold (90% CI, 0.76–1.00) and carbamazepine (CYP3A4 inducer) 0.58‐fold (0.49–0.69), but increased in combination with itraconazole (CYP3A4 inhibitor) 2.11‐fold (1.77–2.53). Imlunestrant had no DDIs with CYP2C8 (repaglinide), CYP2C19 (omeprazole), and CYP3A4 (midazolam) substrates, or a P‐gp inhibitor (quinidine), but weakly inhibited dextromethorphan (CYP2D6 substrate), digoxin (P‐gp substrate), and rosuvastatin (BCRP substrate) clearance. Thus, considerations should be taken when imlunestrant is coadministered with strong CYP3A4 inhibitors, strong CYP3A4 inducers, and CYP2D6 and P‐gp substrates where small changes in exposure may lead to toxicities.
ABSTRACT Gastric cancer and gastroesophageal junction cancer, primarily adenocarcinoma, originate in the stomach's mucosa. Peritoneal metastasis is a common pattern in gastric cancer. The neutrophil‐to‐lymphocyte ratio (NLR) and platelet‐to‐lymphocyte ratio (PLR), as systemic inflammation indicators, change before clinical symptoms, aiding early assessment of pathological changes. Without a single diagnostic method for detecting peritoneal metastasis in stomach and gastroesophageal junction cancer, this study examines the predictive value of NLR, PLR, and the albumin‐to‐derived neutrophil‐to‐lymphocyte ratio (Alb‐dNLR) for peritoneal metastasis. A retrospective observational study was conducted at Kasturba Medical College and Kasturba Hospital, Manipal, in which NLR and PLR were calculated for 168 patients, while Alb‐dNLR was determined for 155 patients due to unavailable variables required for calculation. The cutoff values established for albumin, dNLR, NLR, and PLR were 4.22, 2.577833, 1.832388, and 207.1929, respectively. Patients were categorized into high‐ and low‐score groups using the Youden index, and these groups were subsequently compared for the occurrence of peritoneal metastasis. A statistically significant association was identified between the PLR (p‐value: 0.004) and the NLR with peritoneal metastasis (p‐value: 0.021). Conversely, no statistically significant association was observed between the Alb‐dNLR score and peritoneal metastasis (p‐value: 0.371). Patients exhibiting elevated preoperative NLR and PLR were at an increased risk of developing peritoneal metastasis. The study identified that elevated preoperative NLR and PLR serve as significant predictors of peritoneal metastasis in gastric cancer and gastroesophageal junction cancer. The study identified that elevated preoperative NLR and PLR were significantly associated with peritoneal metastasis in gastric cancer and gastroesophageal junction cancer.
ABSTRACT Phase 2a trials in tuberculosis patients traditionally assess early bactericidal activity over two weeks, often using the time‐to‐positivity biomarker, followed by a phase 2b study typically lasting 8‐week with time‐to‐event of culture conversion as the endpoint. This study investigated different machine learning models to predict the time‐to‐event of 2‐month culture conversion in the REMoxTB trial with phase 2a time‐to‐positivity biomarker data and the impact of different phase 2a study lengths. Time‐to‐positivity at baseline and up to 14 days or 28 days after two moxifloxacin‐containing regimens and one control regimen were analyzed using nonlinear mixed‐effects modeling. The final models were used to predict individual baseline time‐to‐positivity and time‐to‐positivity differences between 0 and 14 days or 0 and 28 days. The individual predictions served as features in the following machine learning analysis. Statistical metrics and Kaplan–Meier plots informed model selection. Bi‐exponential and exponential decay models described the 14‐day and 28‐day time‐to‐positivity data, respectively. In the machine learning analysis, baseline time‐to‐positivity and/or time‐to‐positivity differences were ranked as the most important features in all models. Statistical metrics and Kaplan–Meier plots indicated a good fit to culture conversion at 8 weeks using 4‐week phase 2a information with a C‐support vector classification model. Four‐week time‐to‐positivity phase 2a data provided more information compared to the 2‐week time‐to‐positivity data for the prediction of culture conversion. The workflow demonstrated the potential of machine learning to predict the time‐to‐event of phase 2b culture conversion up to 8 weeks using time‐to‐positivity phase 2a biomarker information in a clinical trial assessed with pharmacometric analysis.
ABSTRACT Drug‐induced QTc prolongation (diQTP) is a major risk factor for torsades de pointes and sudden cardiac death. This study evaluated whether carriers of the G allele of the NOS1AP rs10494366 T > G variant have greater risk for diQTP when prescribed high‐risk QT‐prolonging medications. A retrospective case‐control analysis was conducted using the Michigan Genomics Initiative (MGI) biobank, linking genotype and electronic health records for 5848 patients of European ancestry prescribed ≥ 1 CredibleMeds high‐risk drug for torsades de pointes between 2001 and 2022. QTc was Bazett‐corrected, and diQTP was defined as change of > 60 ms from the baseline QTc and/or QTc ≥ 500 ms. Associations between rs10494366 and diQTP were tested using unadjusted and propensity‐score‐adjusted logistic regression. The minor‐allele frequency (G) was 0.36, and genotype frequencies were in Hardy–Weinberg equilibrium (p = 0.07). diQTP occurred in 12.2% of participants. The G allele was not significantly associated with risk of diQTP in either the unadjusted analysis (OR 0.94 [95% CI 0.84–1.05] p = 0.27) or the adjusted analysis (OR 0.94 [95% CI 0.83–1.06] p = 0.30). In this large, real‐world cohort of patients with European ancestry, the NOS1AP rs10494366 variant was not significantly associated with the risk of diQTP. These findings suggest that previously observed associations between this variant and baseline QTc may not extend to all drug‐induced settings.
ABSTRACT Biologic therapies are traditionally regarded as having low potential for drug–drug interactions (DDIs) due to their large molecular size and limited direct involvement with drug‐metabolizing enzymes and transporters (DMETs). However, accumulating evidence indicates that these drug products may indirectly modulate DMET activity through cytokine‐mediated mechanisms, particularly in the context of inflammatory diseases. Pro‐inflammatory cytokines such as interleukin‐6 have been shown to suppress the expression of cytochrome P450 (CYP) enzymes in vitro, most notably CYP3A4, thereby potentially altering the metabolic clearance of concomitant medications. This phenomenon may be pertinent in conditions such as inflammatory bowel disease (IBD) where chronic inflammation and systemic physiological alterations may significantly influence drug disposition. Although the U.S. Food and Drug Administration provides a decision framework to assess large molecule DDI risk based on the mechanism of action, it does not offer a standardized, mechanistic approach for evaluating such interactions. In response, alternative methodologies such as the use of endogenous biomarkers like 4β‐hydroxycholesterol, a proposed surrogate for CYP3A activity, and pharmacokinetic modeling are in exploration to support DDI risk assessment to potentially avoid dedicated clinical trials in patients. This review evaluates current knowledge on biologics‐mediated DDIs with a particular focus on drug‐disease interactions in IBD. There is an opportunity for a more structured, mechanistically informed evaluation that integrates cytokine profiling and biomarker‐based approaches to identify DDI risk and enhance the overall understanding of the degree of interaction and its clinical significance.
ABSTRACT Given the increasing prevalence of postpartum depression (PPD) and the stigma associated with this condition, it is essential to address this significant health concern. Brexanolone is an FDA‐approved treatment for PPD that works by positive‐allosteric modulation of type A γ‐aminobutyric acid (GABA A ) receptors. As an intravenous version of allopregnanolone, brexanolone is known for its rapid onset of action and lasting impact on depressive symptoms in patients with PPD, with significant reductions in depressive symptoms observed by the end of the 60‐h infusion and improvements maintained through Day 30 of follow‐up. The quick onset is particularly important as traditional first‐line treatment for PPD can take up to several weeks to produce observable improvements. Despite its efficacy, the clinical use of brexanolone is limited by the need for a continuous 60‐h intravenous infusion and mandatory enrollment in Risk Evaluation and Mitigation Strategy (REMS) program due to risks of excessive sedation and sudden loss of consciousness. Brexanolone is metabolized through non‐CYP‐mediated pathways, including keto‐reduction, glucuronidation, and sulfation; therefore, no dose adjustments are required in patients with mild to severe renal impairment (eGFR 15–89 mL/min/1.73 m 2 ). However, its use should be avoided in end‐stage renal disease (eGFR < 15 mL/min/1.73 m 2 ) due to potential accumulation of its solubilizing vehicle, betadex sulfobutyl ether sodium. While primarily indicated for PPD, brexanolone is under investigation for post‐traumatic stress disorder, alcohol use disorder, postpartum psychosis, and tinnitus. Future research focusing on alternative delivery methods and expanded therapeutic indications could make brexanolone more accessible and broaden its clinical utility.
