Molecular Pharmaceutics

BackgroundExtracellular vesicles (EVs) are nano and macro-sized, lipid-bound particles, involved in cellular communication. Interestingly, cancer-derived EVs show a heterologous and cross-species tumour tropism which makes them a potential tool for efficient delivery of therapeutic small interfering RNA (siRNA) to the tumour cells. MethodsEVs derived from glioblastoma cells (U373P and U373vIII) were loaded with EGFRvIII siRNA to develop a targeted therapeutic strategy against glioblastoma. EV biodistribution was evaluated using fluorescent indocyanine green (ICG) staining followed by ex vivo imaging. Different loading strategies, including passive loading, sonication, saponin-mediated membrane permeabilization, electroporation, and transfection were assessed for their efficiency in loading siRNA into EVs. The efficiency of each method was evaluated by nano flowcytometry, in vitro uptake assay followed by immunoblot (western blot) analysis. Eventually, the most effective formulation was tested for the systemic siRNA administration and selective tumour delivery in vivo, followed by evaluation of tumour size and EGFRvIII expression. ResultsHere, we showed that siRNA transfection into EVs was the most effective loading strategy, as confirmed by nano-flow cytometry, uptake assays, and western blot analysis, achieving over 90% knockdown efficiency in vitro for EVs carrying EGFRvIII siRNA. In vivo, EGFRvIII siRNA-loaded EVs homed to the tumour site and downregulated EGFRvIII expression compared with the PBS-siRNA control group; however, no significant tumour shrinkage was observed. ConclusionEGFRvIII-targeting, glioblastoma cell-derived EVs can be used as siRNA delivery carriers for targeted gene therapy in glioblastoma. However, further optimization of siRNA delivery and treatment duration is required.

Anthracycline induced cardiotoxicity is a significant problem for oncologists and cancer patients. The leading cause of non-cancer death in cancer patients and survivors is heart failure, which is frequently attributed to the exposure to chemotherapeutics like anthracyclines. The most notorious of these chemotherapies is doxorubicin, which causes cardiac contractile dysfunction that in some cases is irreversible. In this study, we report the development of NanoDMX, a phosphatidylserine-containing liposomal formulation of DMX5804, a small molecule inhibitor of MAP4K4, and demonstrate that its administration prevents doxorubicin-induced left ventricular dysfunction in mice. Additionally, we demonstrate that DMX-5804 protects cardiomyocytes in vitro through a combination of mechanisms outside of the expected route of suppressing the JNK pathway. Overall, we demonstrate that the use of NanoDMX, a novel liposomal system using both DMX-5804 and phosphatidylserine, can prevent the damage induced by doxorubicin over the course of a single high dose in vivo model.

Background/ObjectivesDevelopability assessment is a critical step in advancing antibody-based molecules toward clinical application. This evaluation typically begins during clinical candidate selection and continues throughout all modifications of the molecule during development. It is guided by the target product profile, which includes the intended administration route and regimen, formulation parameters, and process conditions encountered during manufacturing, storage, and delivery. While developability testing is well established for conventional therapeutic antibodies, strategies for assessing single-domain antibodies (sdAbs) and their conjugates remain underexplored. Here we present a strategy to test the developability of sdAbs as a case study for two clinical candidates intended as precursors for the production of diagnostic tracers for clinical imaging. MethodsAssays were developed to evaluate chemical and thermodynamic stability, target binding affinity and capacity, and chelation efficiency ("chelatability"). Accelerated stability studies were conducted for both unconjugated sdAbs and their chelator conjugated forms following incubation at two pH conditions, at multiple time points, and after twelve freeze-thaw cycles to simulate process conditions and long-term storage. Analytical assays were applied stepwise in a hierarchical approach to minimized experimental effort and material consumption. Candidates exhibiting critical developability features were selectively addressed by assays with increasing precision. ResultsA tailored panel of analytical assays optimized for low molecular weight proteins was established and applied to the two clinical candidates, identifying instability hotspots as well as potential mitigation strategies. Successful engineering of a candidate with an initially critical developability profile was achieved. ConclusionThis study demonstrates the implementation of a structured developability assessment strategy for sdAb conjugates. The approach integrates physicochemical and functional stability evaluations, supporting robust candidate selection, formulation development, and method optimization for this class of molecules.

Pancreatic ductal adenocarcinoma (PDAC) remains difficult to treat with nucleic acid therapeutics because efficient intratumoral delivery is limited and off-target liver accumulation is common. Here, we developed a structure-activity map for intraperitoneally administered mRNA lipid nanoparticles (mRNA-LNPs) to identify formulation features that improve delivery to pancreatic tumors while reducing liver expression. A full-factorial library of 48 mRNA-LNP formulations was generated by varying ionizable lipid, sterol, phospholipid, and PEG-lipid components. Formulations were characterized for size, polydispersity, zeta potential, and encapsulation, then evaluated in an orthotopic KPC8060 pancreatic tumor model after intraperitoneal administration of firefly luciferase mRNA-loaded LNPs. Biodistribution was assessed by Rhodamine B fluorescence and functional delivery by luciferase expression 12 h after dosing. Lipid composition strongly influenced both physicochemical properties and in vivo performance. G0-C14-based formulations produced the smallest and most homogeneous particles, whereas FTT5-containing formulations were generally larger. Across the 48-formulation library, mRNA expression and nanoparticle biodistribution varied significantly among tumor, pancreas, liver, and spleen. Statistical, decision-tree, and predictive modeling analyses identified composition rules associated with organ-selective delivery. High tumor expression was associated primarily with G0-C14 combined with DSPC and {beta}-sitosterol, whereas liver expression was favored by C12-200 or DLin-MC3-DMA with DOPE and DSPE-PEG. Notably, a G0-C14/DSPC/DSPE-PEG formulation emerged as a lead candidate, producing a greater than 6-fold increase in tumor luciferase signal relative to the library median while reducing liver exposure by approximately 60%. Histopathology showed no treatment-related liver or lung toxicity. These findings define actionable formulation rules for tuning intraperitoneal mRNA-LNP delivery in PDAC and support further development of tumor-selective mRNA therapeutics for pancreatic cancer.

Gastric cancer is among the most common cancers and represents a major public health problem worldwide. New therapeutic strategies and drugs are needed. Anti-angiogenic agents targeting the Vascular Endothelial Growth Factor (VEGF) are used in combination therapy in the clinic, although their efficacy remains modest. We believe that these large anti-VEGF antibodies could be advantageously replaced by smaller peptides with better tissue penetration. In this study, we evaluate the efficacy of a previously described dimer peptide ligand of VEGF, D6, in inhibiting the proliferation of gastric cancer cells and the growth of the corresponding murine xenograft. The activity of the D6 peptide in these assays was comparable to that of bevacizumab, the positive control antibody, although the peptide required repeated injections at higher molar concentrations. These promising results justify the continued optimization of the peptide dimer, currently under investigation in our laboratory.

Aminopyridines, including 4-aminopyridine (4AP), 3,4-diaminopyridine, and [18F]3-fluoro-4-aminopyridine, are voltage-gated potassium (KV) channel blockers used clinically to enhance conduction in neurological disorders and to image demyelination by PET. Developing new aminopyridines may yield improved therapeutics or imaging agents. Here, we characterized the physicochemical properties (pKa, log D), KV channel-blocking activity, toxicity (LD50), and pharmacokinetics of a novel compound, 4-methyl-3-aminopyridine (4Me3AP). 4Me3AP was less basic and more lipophilic than 4AP and showed greater blocking potency across multiple KV channels expressed in Xenopus oocytes. In mice, 4Me3AP exhibited lower acute toxicity (LD50= 29.3 mg/kg) than 4AP (LD50= 12.7 mg/kg) and a longer plasma half-life. These findings indicate that 4Me3AP is a potent KV channel blocker with favorable pharmacological properties, supporting its potential for symptomatic treatment of demyelinating diseases.

Background: Mucopolysaccharidosis type IIIA (MPS IIIA; Sanfilippo syndrome) is a fatal neurodegenerative lysosomal storage disorder caused by impaired degradation of heparan sulfate (HS). Despite rapid advances in gene and enzyme therapies, there remains a critical need for an analytically validated, quantitative biomarker that accurately reflects central nervous system (CNS) substrate burden. Such biomarker would be a valuable tool in assessing disease progression and monitoring therapeutic efficacy. Objective: This study describes the method development, fit for purpose validation, and preliminary clinical application of a quantitative liquid chromatography-mass spectrometry (LC-MS/MS) assay for the HS-derived disaccharide N-sulfoglucosamine-glucuronic acid (GlcNS-GlcUA) in human cerebrospinal fluid (CSF), a critical biomarker for diagnosis, disease monitoring, and regulatory evaluation of emerging MPS IIIA therapies. Methods: A structurally defined GlcNS-GlcUA reference standard and its [13C6]-labeled internal standard were used in a derivatization and detection workflow employing 1-phenyl-3-methyl-5-pyrazolone labeling, and LC-MS/MS. Results: The method exhibited acceptable linearity across 0.005-0.500 nmol/mL (r[≥]0.9976), with intra- and inter-assay imprecision [≤]3.5%CV and accuracy within 95%-110% of nominal concentrations. No matrix or hemolysis interference or carryover was observed, and the analyte remained stable during freeze-thaw storage conditions. Application of the method to 12 CSF samples from patients with MPS IIIA demonstrated quantifiable GlcNS-GlcUA levels ranging from 0.0054 to 0.106 nmol/mL, confirming suitability for clinical and regulatory use. Comparison of the MPS IIIA sample results between the development laboratory and the contract research organization laboratory support robust inter-lab assay transfer. Conclusions: This validated LC-MS/MS method establishes a regulatory-grade quantitative assay for measurement of CSF HS in MPS IIIA. Its high analytical sensitivity and reproducibility enable reliable assessment of CNS substrate reduction and pharmacodynamic response, supporting biomarker-driven therapeutic development and accelerated approval pathways for neuronopathic mucopolysaccharidoses.

BackgroundMET overexpression is associated with poor prognosis in many solid tumors due to its central role in tumor survival, invasion, metastasis, and chemoresistance. While targeting MET with antibody-drug conjugates has shown promising results, engineered cellular immunotherapeutic approaches have not been extensively explored. Compared to conventional single-chain variable fragments (scFv), naturally occurring single-domain antibodies consisting of variable heavy chains only (VHH or nanobodies) are smaller, retain high specificity, and exhibit remarkable biochemical stability. In this study, we tested the efficacy of MET-targeting VHH-CAR-T (chimeric antigen receptor T cells). MethodsWe generated a panel of VHH-CAR-Ts using mRNA electroporation. VHH-CAR-T cells were evaluated in functional assays including cell binding avidity, cytokine production profiles, hydrogel microwell-based cellular kinetics, and in vitro cytotoxicity. We also assessed the therapeutic efficacy of VHH-CAR-T in an in vivo mouse model of metastatic triple negative breast cancer (TNBC). ResultsAmong the tested VHH, we identified those with intermediate avidity as most effective for in vitro tumor killing. VHH-CAR-Ts with CD28 costimulatory domains demonstrated augmented cytotoxicity with favorable selectivity, requiring a minimum antigen density threshold for activation. Mechanistically, VHH-CAR-Ts demonstrated low tonic signaling, high avidity, potent cytokine production, and rapid tumor killing kinetics. When administered in an mRNA format, VHH-CAR-Ts exhibited potent and prolonged control of tumor growth in an in vivo metastatic model of TNBC. ConclusionTaken together, these results demonstrate that VHH-CAR-Ts exhibit robust MET specificity and potent therapeutic efficacy both in vitro and in vivo. Thus, VHH-CAR-T cell therapy represents a promising immunotherapeutic strategy for targeting MET-overexpressing solid tumors. What is already known on this topicMET signaling is an important contributor to the aggressiveness of many solid tumors, and targeting MET by antibody-drug conjugates has shown efficacy and safety. Targeting MET by CAR-T cells has been under study, though with limited potency. What this study addsThis study is the first to demonstrate effectiveness of anti-MET VHH-CAR-T cells. Compared with other antigen binding domains, VHH-incorporated CAR-T cells show low tonic signaling, a favorable cytokine profile, and potent tumor killing. How this study might affect research, practice or policyWith the multiple advantages of VHHs including small size, stability, and low potential for tonic signaling, VHH-CAR-T cells represent a promising approach for CAR-T design against solid tumors.

Bacteriophage therapy is being explored as an alternate therapeutic approach for treating drug- resistant bacteria, including mycobacteria. However, rational phage dosing remains limited by scarce pharmacokinetic (PK) data and an incomplete understanding of tissue distribution. We performed dose-ranging studies in mice of three therapeutic mycobacteriophages (BPs{Delta}, ZoeJ{Delta}, Muddy) after intravenous (IV) and intratracheal (IT) administration. All phages behaved similarly. IV dosing produced biphasic kinetics with non-proportional exposure and declining tissue-to-plasma ratios, indicating saturable uptake and elimination. IT delivery yielded monophasic profiles with [~]390-fold higher lung exposure and [~]490-fold lower plasma exposure, supporting inhaled therapy for pulmonary mycobacterial infections. Using BPs{Delta} data, we developed a mechanistic PBPK model incorporating transcytosis, saturable host clearance, plasma elimination, and lymphatic transport. The model accurately predicted ZoeJ{Delta} and Muddy PK, enabled cross-species extrapolation, and showed that phage morphology influences disposition. This framework advances phage therapy toward model-informed, exposure-guided dose and route selection for multidrug-resistant bacterial infections.

BackgroundMultidrug-resistant (MDR) Gram-negative bacteria have triggered a critical global health crisis. Polymyxin lipopeptide antibiotics are used as a last-line therapy against these problematic pathogens, but their clinical use is largely limited by severe nephrotoxicity. Human oligopeptide transporter 2 (hPepT2) is a membrane transporter mediating the reabsorption of polymyxins in renal proximal tubular cells, substantially contributing to their nephrotoxicity. However, it remains unclear how polymyxins interact with hPepT2. MethodsIn this study, we investigated the structure-interaction relationship (SIR) of polymyxins with hPepT2 by integrating computational, chemical and cell biology approaches. Bioinformatic modelling predicted the residues essential for the binding of polymyxins with hPepT2. Transporter mutagenesis and molecular analysis were employed to explore the role of each residue in the interaction of hPepT2 and polymyxins. Moreover, we synthesised a series of polymyxin-like analogues with altering the moieties that are critical for binding with hPepT2. The antibacterial activity and nephrotoxicity of these analogues were subsequently assessed. ResultsOur bioinformatic modelling proposed an outward-facing structure of hPepT2 with a possible transport pathway that polymyxins bind to the lateral opening site of hPepT2 (e.g. E214, D215, D317, D342, E622). Molecular assays for transporter function and expression confirmed that D215 residue of hPepT2 is critical for polymyxin binding, while several other residues significantly impact on transporter turnover rate and/or protein expression. Our experimental validations showed that the lipopeptide analogues with altering the Dab1, Dab3, Dab5 and Dab9 moieties of polymyxins demonstrated decreased interactions with hPepT2. Among these synthetic analogues, alanine substitution at Dab3 showed reduced nephrotoxicity in mice while reserved antibacterial activity against a range of bacterial strains. ConclusionsOverall, this proof-of-concept study demonstrated that the computationally predicted and experimentally validated polymyxin-hPepT2 SIR model provides a viable approach for the discovery of novel, safer lipopeptide antibiotics.

Oral microemulsions are one drug delivery system often implemented to improve intestinal permeability and oral bioavailability of poorly-water soluble drugs. They also present several practical advantages including high patient compliance and simplified manufacturing methods which contribute to their promise as marketable drug products. Despite these advantages, however, the microemulsion formulation development process is extremely time consuming and resource intensive, typically involving extensive screening of components and excessive preliminary trials in order to achieve stable formulations. As a result, MEs are often suboptimal in their therapeutic performance, and there is an unmet need for improved methods to streamline microemulsion formulation design. In this work, a batch Bayesian Optimization strategy was used to design a subset of unique in-specification microemulsions with highly optimized physicochemical properties in five iterations containing batches of five experiments. A two-phase modeling approach was developed to achieve this goal and allowed for navigating a complex experimental design space, including multiple oils, surfactants, cosurfactants, and processing parameters with a training dataset consisting of 22 experiments. As a result of this study, five high-performing blank microemulsions were identified, and four of them exhibited physical stability upon storage for up to 30 days. Further, when loaded with two different model drug candidates, three of the microemulsions achieved high drug loading, acceptable stability and improved in-vitro permeability, highlighting their promise for potential translation to later stages of development.

Messenger RNA (mRNA) vaccines using lipid nanoparticles (LNPs) are well-established and globally approved with acceptable safety profiles for preventing respiratory disease. Other mRNA-LNP product concepts are also emerging as novel treatments for broader clinical use. Here, we describe mRNA-LNP vaccine tissue distribution and kinetics after intramuscular dosing using three products formulated with same LNP matrix: mRNA-1273 (Spikevax), mRNA-1647 (a candidate cytomegalovirus [CMV] vaccine), and a reporter mRNA (nascent peptide-luciferase) drug product. Consistent biodistribution patterns were observed across studies: tissues with highest exposures were the injection site, draining lymph nodes, and spleen, with minimal distribution to non-lymphoid tissues. Vaccine components cleared rapidly from circulation and tissues, with complete elimination simulated to occur by [~]2 weeks. Following mRNA-1273 vaccination, Spike protein levels were transiently observed (elimination <5 days) and did not accumulate with repeated dosing. The ionizable lipid in the LNP matrix, Lipid H, underwent biotransformation and was excreted renally and hepatically, with no human-specific metabolites. Collectively, these results indicate that the LNP composition, not mRNA cargo, governs biodistribution. Furthermore, in a SARS-CoV-2 infection-free model, there was no evidence of Spike protein persistence. Overall, the data establish a framework that justifies leveraging biodistribution data across products and supports eliminating redundant animal studies.

IntroductionAdverse drug reactions (ADRs) remain a major public health issue, and genetic factors contribute importantly to interindividual variability in drug response. Pharmacogenetic testing helps reduce ADR risk by optimizing drug selection and dosage, particularly in monogenic disorders. Material and MethodsWhole-exome sequencing of 6,739 samples from the Russian population was performed using the MGIEasy Universal DNA Library Prep Set on the DNBSEQ-G400 platform (MGI). Variants in 48 genes were examined, focusing on inherited arrhythmias (Long QT syndrome, Short QT syndrome, Timothy syndrome, Andersen-Tawil syndrome, Brugada syndrome, Atrial fibrillation, Catecholaminergic polymorphic ventricular tachycardia), enzyme deficiencies (Glucose-6-Phosphate Dehydrogenase Deficiency [G6PDD], Porphyrias), Dravet Syndrome (DS) and Malignant Hyperthermia (MH). All identified variants had been reported at least once as pathogenic (P) or likely pathogenic (LP) in ClinVar, along with those occasionally classified as variants of uncertain significance (VUS). Each variant was manually re-evaluated according to ACMG criteria. ResultsA total of 75 unique variants in 18 genes were observed in 119 individuals (1.77%), including 21 carriers and 13 women with a G6PD mutation. Of these, 46 variants were classified as P, 21 as LP, and 8 as VUS. Missense variants accounted for the largest proportion (73.33%). The most affected genes were KCNQ1 (24/119), which exhibited the highest number of unique variants (18), G6PD (20/119), SCN1A (15/119), and RYR1 (14/119). Regarding associated conditions, mutations linked to arrhythmias were found in 51 individuals, MH in 27, G6PDD in 20, DS in 15, and Porphyrias in 6. ConclusionsIncorporating genetic information on both common and rare clinically actionable variants into therapeutic decision-making has the potential to improve medication safety, reduce preventable ADRs, and enhance the effectiveness of personalized pharmacotherapy.

The hollow fiber infection model (HFIM) is a translational in vitro model that links time-varying human pharmacokinetic profiles to the associated viral dynamic responses, from which pharmacokinetic/pharmacodynamic (PK/PD) targets can be derived. Establishing such targets is essential for antiviral dose selection and optimization. This is particularly important for cytomegalovirus (CMV) infection treatment, which primarily affects vulnerable patient populations. PK/PD targets for ganciclovir, the first-line drug for treatment, are not yet defined. The lack of an undefined PK/PD target makes dose optimization challenging and may result in suboptimal exposure, prolonged toxicity, and the emergence of resistance. For the first time, we have demonstrated the use of a low-cost hemodialyzer hollow fiber cartridge with application for CMV infection using ganciclovir. We have established a system that 1) supports CMV culture for PD analysis, 2) reproduces a clinically relevant ganciclovir PK profile, and 3) maintains consistent drug exposure in the infected cells, allowing reliable PK/PD analysis. Quantitative methods such as tissue culture infectious dose 50% (TCID50) and quantitative PCR were used to assess both active virus replication and genome copies production. Ganciclovir PK was measured using liquid chromatography-tandem mass spectrometry (LC-MS/MS). This validation study serves as a fundamental step that can allow further PK/PD studies for ganciclovir and other antiviral agents that is still largely understudied. Consequently, this model could provide an affordable and practical platform for establishing clinically relevant PK/PD targets and guide treatment optimization.

This work develops and characterises a hierachichal oral drug delivery system based on the microencpasulation of drug-loaded amphiphilic nanogels within a mucoadhesive alginate/chitosan shell. Results show a more controlled release and a statistically significant oral half-life with respect to the free drug.

Medulloblastoma (MB) is an aggressive central nervous system (CNS) malignancy that primarily affects children and frequently exhibits metastasis to the leptomeninges of the brain and spinal cord. We developed a {beta}-Cyclodextrin-poly({beta}-Amino Ester) nanoparticle system to deliver the histone deactylase inhibitor (HDACi) Panobinostat to MB by the intrathecal route. Various imaging methods were utilized to study nanoparticle and payload fate following infusion into the cerebrospinal fluid (CSF) of mice via cisterna magna or lumbar access points. Nanoparticles dramatically improved penetration of hydrophobic small molecules into distal regions of the spinal cord. Panobinostat-loaded nanoparticles were effective at treating patient-derived MB, activating pharmacodynamic targets, slowing growth of the primary tumor, decreasing incidence of metastasis at the time of death, and ultimately prolonging survival. These studies provide insight into the mechanisms mediating transport of colloids and therapeutic molecules in the subarachnoid space and highlight new approaches for treating metastatic disease in the CNS.

BackgroundReliable population pharmacokinetic (PopPK) parameter estimation can be compromised by outliers under Gaussian residual error models. A common mitigation strategy is post hoc filtering based on conditional weighted residuals (CWRES); however, this approach can be insensitive due to model "masking" driven by variance inflation. Practical barriers to implementing robust likelihoods in standard software have motivated interest in computationally simpler exponential-tail alternatives such as the Laplace and exponential power distribution (EPD). MethodsWe implemented a one-compartment PopPK model using a custom likelihood workaround in Monolix to benchmark four residual error distributions: Normal, Laplace, Generalized Error Distribution (GED), and Students t. We assessed CWRES sensitivity under extreme contamination and compared estimation performance using theoretical tail-behavior analysis, controlled simulation studies spanning multiple contamination severities, and a real-world caffeine PK case study with influential terminal-phase deviations. ResultsSimulations showed that CWRES-based diagnostics can be unreliable: extreme outliers frequently produced |CWRES| < 6 because the Normal model inflated residual variance, thereby masking contamination. Exponential-tail models (Laplace, GED) improved robustness for mild to moderate outliers but failed under extreme deviations due to insufficiently heavy tails compared to power-law decay. In contrast, the Students t model, via power-law tail behavior, maintained stable and minimally biased structural parameter estimates across contamination scenarios. Consistent patterns were observed in the caffeine case study, where the Students t model provided improved fit and physiologically plausible parameter estimates. ConclusionsCWRES-driven outlier handling is methodologically fragile because influential contamination can be masked by variance inflation and induce biased inference. Among robust residual error models, exponential-tail distributions may be insufficient for extreme outliers, whereas the Students t distribution provides more stable inference across contamination severities. These findings support adopting Students t residual modeling as a default robust option in routine PopPK workflows when outlier contamination is plausible.

BackgroundPersonalized pharmacotherapy requires systematic consideration of genetic factors influencing drug efficacy and safety. The accumulation of large-scale whole-exome sequencing (WES) data provides an opportunity to assess population frequencies of clinically significant pharmacogenetic variants; however, the diagnostic applicability of exome data for pharmacogenomics remains insufficiently studied. Materials and MethodsA retrospective analysis of 6,102 anonymized sequencing datasets obtained between 2020 and 2025 was performed using the DNBSEQ-G400 (MGI) platform and Agilent SureSelect Human All Exon v6/v7/v8 enrichment kits. SNV and indel detection, CNV analysis, high-resolution HLA typing, and diplotype assignment for key pharmacogenes were conducted. Pharmacogenomic annotations were derived from PharmGKB (levels of evidence 1A-2B), CPIC, and PharmVar. Additionally, WES limitations and the feasibility of imputing non-coding pharmacogenetic variants were evaluated. ResultsPopulation frequencies of alleles and metabolic phenotypes were determined for 13 Very Important Pharmacogenes (VIPs), along with the distribution of HLA class I and II alleles. The highest allelic and phenotypic variability was observed in CYP family genes, particularly CYP2D6, CYP2C19, and CYP2B6. A total of 663 pharmacogenomic annotations were identified, predominantly related to drug metabolism (50.38%) and toxicity (29.56%), including psychotropic agents, anticoagulants, statins, opioid analgesics, antineoplastic agents, and immunosuppressants. At least 32 drugs require pharmacogenetic testing based on variants located in non-coding regions, as well as accurate CYP2D6 copy number determination. Linkage disequilibrium analysis demonstrated the inability to reliably impute most non-coding pharmacogenetic variants from WES data. ConclusionThese findings represent one of the largest reference assessments to date of pharmacogenetically significant variant and HLA allele frequencies in the Russian population. The results confirm the utility of WES for population pharmacogenomic screening while simultaneously highlighting its fundamental limitations and the need for alternative genetic diagnostic methods in selected cases.

Steady-state volume of distribution (Vss) can be predicted using tissue-to-plasma partition coefficients (Kp) and tissue volumes. Kp values are important components of physiologically based pharmacokinetic (PBPK) models, allowing for estimation of distribution kinetics and simulation of concentration-time profiles. Many in silico approaches have been developed to predict tissue Kp values based on physicochemical processes that govern drug distribution. However, these methods frequently over- or under-predict tissue Kp values, highlighting the need to consider additional mechanisms that can impact drug distribution kinetics. Many drugs have been shown to bind to rat and human fatty acid binding proteins (FABPs) in vitro but the impact of this binding to drug distribution has not been incorporated into Kp predictions. We hypothesized that incorporating intracellular protein binding into tissue Kp predictions will improve Kp prediction accuracy. Using liver as a model organ, four physiologically based dynamic liver distribution models (LDMs) were developed to assess the role of distribution processes in Kp predictions. The developed LDMs incorporated known distribution mechanisms and intracellular drug binding to liver FABP (FABP1). The liver Kp values for drugs that bind to FABP1 were accurately predicted using the LDM that incorporates lipid partitioning, albumin distribution, and FABP1 binding but not using LDMs without FABP1 binding. Human FABP1 expression was quantified in 61 human livers and the interindividual variability in tissue FABP1 binding was incorporated into tissue Kp predictions. These simulations showed that intracellular FABP1 binding can cause interindividual variability in Kp values and result in concentration dependent tissue distribution. Significance StatementThis study shows that incorporating intracellular protein binding such as binding to FABP1 into tissue Kp predictions improves accuracy of the predictions. The novel dynamic LDM can be extrapolated to other organs of interest and integrated into full-body PBPK models to predict drug distribution kinetics. With dynamic and saturable distribution mechanisms incorporated into a PBPK model, nonlinear distribution kinetics can be simulated for various drugs.

First-in-human (FIH) dose selection for monoclonal antibodies (mAbs) typically relies on deterministic allometric scaling but lacks formal uncertainty quantification. While Bayesian methods have been widely applied in population PK modeling and dose individualization, their use for propagating uncertainty through allometric scaling in mAb FIH dose selection has not been systematically explored. This is a critical limitation for molecules with narrow therapeutic windows, such as CNS-targeted mAbs, where the blood-brain barrier restricts IgG penetration to [~]0.1-0.3% of plasma concentrations, requiring high systemic doses that must be balanced against dose-limiting toxicities. To provide uncertainty-aware FIH dose recommendations, we developed and systematically evaluated a Bayesian hierarchical PK framework tested on CNS mAbs. By simultaneously learning population-level PK distributions and allometric scaling relationships from 9 well-characterized mAbs, the model propagates inter-antibody variability and scaling imprecision into full posterior predictive distributions. For validation, the framework was applied to 3 Alzheimers disease mAbs, donanemab, lecanemab, and aducanumab, using only cynomolgus monkey PK data to predict human outcomes. Leave-one-out cross-validation yielded a mean absolute prediction error of 11.6% for human clearance. Predicted FIH doses were 10 mg/kg for donanemab and lecanemab, and 30 mg/kg for aducanumab. Retrospective comparison with clinical data showed prediction errors of -36.1%, -36.1%, and -15.7%, respectively, all within two-fold of observed values. The systematic under-prediction of clearance is attributable to target-mediated drug disposition not captured by the linear model. However, this bias is pharmacologically conservative, as it over-predicts systemic exposure to ensure wider safety margins. This framework enables risk-informed FIH dose selection by providing full probability distributions of predicted exposures rather than point estimates.