Biosimilars expand access to biologic therapies by offering high quality, cost-effective alternatives to originator products. Analytical similarity assessment is central to biosimilar development, yet biosimilarity outcomes are strongly influenced by reference product (RP) lot numbers, assay variability and RP variability, and statistical methodology. The World Health Organization (WHO) recommends statistical tools for analytical biosimilarity evaluation, including X-sigma, min-max, and tolerance interval test, however their sensitivity to variability has not been systematically examined. In this perspective, we examine how reference lot selection, assay replication, and RP heterogeneity affect biosimilarity conclusions when applying WHO-recommended methods. Using analytical data across glycosylation, size heterogeneity, and charge variant, four biosimilars were compared with an innovator RP under different variability scenarios. Similarity outcomes were quantified using a normalized scoring framework. Increasing RP batches and incorporating broader intrinsic RP variability increased apparent biosimilarity by yielding acceptance limits that better reflected reference dataset dispersion, whereas limited sampling produced unstable variance estimates and restrictive criteria. Increased assay replication provided modest, attribute-dependent changes in biosimilarity outcomes. Across scenarios, X-sigma approaches were more robust to limited sampling and changes in variance estimates, whereas min-max and tolerance interval methods were more sensitive to dataset expansion and distributional extremes. These findings support data-driven justification of reference characterization and statistical strategy in biosimilarity assessment.

Nilotinib is reported to have a reduced maximal drug concentration (Cmax) in patients with hepatic impairment (HI). Given this compound is poorly soluble, highly lipophilic and has a positive food effect, reduced absorption in HI may be due to impaired micelle-mediated solubility resulting from reduced intestinal bile salts. PBPK modelling can be used to test this hypothesis as well as being a diagnostic tool to elucidate other potential mechanisms not captured by the base model. In this study, a mechanistic PBPK model for nilotinib was developed and its performance was evaluated in HI patients. A 'learn-and-confirm' approach was taken, using clinical food effect data to validate the model's mechanistic sensitivity to bile salt-mediated solubility. The final model recovered clinical studies well, with area-under-the-curve (AUC) and Cmax being within 0.8 - 1.25-fold for fasted studies and 1.5-fold for fed/fasted ratios. AUC and Cmax were well recovered in mild HI (within 0.8 - 1.25-fold) and reasonably well recovered in moderate HI (within twofold) but significantly overpredicted in severe HI (> twofold). Bile salt concentrations were reduced across gastrointestinal segments for moderate and severe HI in line with in vitro data. Following twofold and 2.5-fold reductions in bile salts in moderate and severe HI, respectively, sensitivity was minor. Given uncertainty around the fugut, this was reduced to better recover the Cmax (within two-fold). PBPK modelling can be used as a diagnostic tool to evaluate impaired mechanisms in HI.

Skin cancer continues to pose a significant global health burden, with conventional therapeutic modalities limited by systemic toxicity, poor tumor selectivity, and the development of therapeutic resistance. Glucosinolates (GLs) and their hydrolyzed product, sulforaphane, have attracted considerable interest as chemopreventive and therapeutic agents owing to their potent antioxidant, anti-inflammatory, and pleiotropic anticancer activities. However, the clinical translation of these phytochemicals is severely hindered by their chemical instability, rapid metabolism, and poor bioavailability. Nanocarrier systems, particularly biodegradable polymeric nanoparticles such as poly(lactic-co-glycolic acid) nanoparticles, offer a promising strategy to overcome these barriers by enhancing compound stability, improving bioavailability, and enabling tumor-targeted delivery via the enhanced permeability and retention effect. This review critically evaluates recent progress in the development of GL and sulforaphane-loaded nanocarriers, detailing their physicochemical properties, in vitro and in vivo anticancer efficacy, and safety profiles, highlighting their potential to advance more effective and less toxic therapeutic strategies for skin cancer prevention and treatment.

While accurately predicting the human brain penetration of drugs has been notoriously challenging, recent physiologically based pharmacokinetic (PBPK) modeling work has attempted to integrate passive permeability, active transport, and physiological parameters to support central nervous system (CNS) exposure predictions. In this study, a bottom-up PBPK approach was utilized prospectively to predict human cerebrospinal fluid (CSF) concentrations of fenebrutinib. In vitro permeability, efflux transporter, and brain binding data were generated and extrapolated in vitro to in vivo for required inputs in a five-compartment mechanistic brain PBPK model. The approach was first assessed using a compound with publicly available clinical CSF data, which informed the use of empirical scalars to address possible in vitro to in vivo translation gaps. The refined bottom-up PBPK approach was then used to predict the CSF concentrations of fenebrutinib. These predictions aligned well with observed CSF data as predicted values fell within 1.5 fold of the observed measurements, indicating the fenebrutinib has good brain penetration in multiple sclerosis (MS) patients, which were available afterward. This work demonstrates the utility of PBPK modeling to enable prospective CNS exposure prediction and support early clinical decision-making.

Accessible tools for pharmacokinetic education and routine clinical pharmacokinetic analysis remain limited. Commercial software carries licensing costs and on the other hand existing open-source alternatives lack integrated workflows spanning study planning through bioequivalence assessment and are not user-friendly. We developed NCA Assistant, a browser-based application for non-compartmental pharmacokinetic analysis, bioequivalence testing, and power and sample size calculations within a single platform designed for pharmacokinetic research and teaching. NCA Assistant was designed with Anthropic Claude AI as coding assistant, with multi-expert prompting. The app was developed in R/Shiny with a hub-based architecture connecting six workflow modules. Core computations rely on established R packages for non-compartmental analysis, power and sample size calculations and analysis of variance. For the interface, there was an emphasis for transparency through interactive visualization, configurable analysis settings and exportable audit trails. The application supports extravascular and intravenous dosing scenarios, handles both single-subject and batch analyses and accommodates standard crossover, replicate, and parallel bioequivalence designs.A dedicated visualization module generates publication-ready concentration-time plots. Features include interactive terminal half-life review with point selection override and configurable thresholds for elimination half-life calculation. A validation package, consisting of draft validation documents and a validation script, accompanies the application and is supplied in the supplemental materials. NCA Assistant provides an accessible entry point for pharmacokinetic data analysis suitable for graduate training and routine clinical pharmacology applications, with extensibility for use in more regulated environments.

Durvalumab monotherapy as a consolidation treatment for limited-stage small-cell lung cancer (LS-SCLC) demonstrated a favourable benefit-risk profile in the ADRIATIC study. We evaluated population pharmacokinetics (PopPK) and exposure-response (E-R) relationships of durvalumab in patients with LS-SCLC. The durvalumab PopPK model was updated by integrating PK data from the ADRIATIC study. Individual exposure metrics were derived from empirical Bayes estimates for E-R analyses relating to safety (adverse events [AEs]) and efficacy (progression-free survival [PFS] and overall survival [OS]). A two-compartment model with a time-dependent clearance adequately characterized durvalumab PK data. The typical parameter estimates for baseline clearance, central and peripheral volume of distribution were 0.273 L/day, 3.49 L and 2.35 L, respectively. All identified covariates are considered to have minimal clinical impact on durvalumab PK parameters with effect within 80-125% of typical values. Kaplan Meier plots of PFS or OS stratified by exposure quartiles suggested a plateaued relationship between durvalumab exposure and PFS or OS at the selected dose level of 1500 mg. The Cox PH model analysis did not identify exposure metrics as significant prognostic factors for the OS or PFS hazard. At 1500 mg IV Q4W, a flat E-R relationship was observed between durvalumab PK exposure and safety endpoints including Grade 3 + treatment-related AEs, Grade 3 + treatment-related AEs of special interest, and AEs leading to treatment discontinuation. The results support the use of durvalumab monotherapy for the patients with LS-SCLC, and no dose adjustment is considered necessary based on the PopPK and E-R analyses. NCT03703297.

Tissue factor pathway inhibitor (TFPI) is an endogenous inhibitor of the extrinsic coagulation pathway. Marstacimab (PF-06741086), a human immunoglobulin G1 monoclonal antibody, binds the TFPI Kunitz-2 domain to improve hemostatic control in individuals with hemophilia. Quantifying the free form of TFPI, i.e., not bound to marstacimab or endogenous binding proteins, is the most direct assessment of target coverage and modulation following treatment. This investigation describes efforts to develop a quantitative assay to measure free TFPI in human plasma to support the marstacimab drug development program. Three protein enrichment methods (competitive antibody capture, binding partner capture, and drug depletion) were assessed in combination with liquid chromatography-tandem mass spectrometry to develop a bioanalytical free target assay. Surface plasmon resonance (SPR) was used to determine the binding affinities of marstacimab and competitive antibody clones to TFPI. SPR analysis demonstrated marstacimab binds TFPI with a nanomolar affinity (equilibrium dissociation constant [KD], 3.7 nM), whereas antibody clones had picomolar binding affinities. All assay formats over-recovered TFPI, indicating disruption of the marstacimab:TFPI complex during sample handling and release of bound TFPI, under the experimental conditions reported here. As a result, accurate measurement of free TFPI was not feasible. These findings demonstrate that the sample manipulation required for analysis altered the binding equilibrium of the marstacimab:TFPI complex with release of bound TFPI that compromised assay specificity. In conclusion, the accurate measurement of free TFPI from marstacimab-treated human plasma was not achievable with the approaches tested, in part because marstacimab binds TFPI with a low-nanomolar affinity.

Accurate measurement of therapeutic protein concentration is critical for ensuring manufacturing consistency. Synonymous gene recoding is often used to improve protein yield, including for coagulation Factor IX (FIX), used to treat Hemophilia B. Although synonymous recoding does not alter primary amino acid sequence, it can affect protein conformation, potentially influencing the accuracy of protein quantification depending on the method used. In this study, we investigated whether synonymous changes in F9 gene, which encodes FIX, impacted reliability of commonly used protein quantitation methods. HEK293- FlpIn cells were used to generate FIX proteins expressed from seven different synonymous F9 sequences. Purified proteins were initially measured using absorbance at 280 nm, followed by quantification using various colorimetric, and fluorometric protein assays. Relative differences among protein quantification methods were inconsistent between wild-type and synonymous F9 variants. Compared with A280 measurements, mean differences across variants ranged from - 33% to - 8% for Bradford and from 14 to 54% for BCA, whereas wider ranges were observed for OPA (- 23% to 133%), CBQCA (14% to 282%), and Lowry (1% to 185%). Bradford and BCA also exhibited lower intra-assay variability, with standard deviations of 6% and 10%, respectively, compared with 12%, 18%, and 33% for OPA, CBQCA, and Lowry assays. These findings indicate that synonymous codon substitutions influence protein quantification outcomes, with Bradford and BCA producing more consistent results and lowest variability, whereas amine-reactive fluorescence-based assays (OPA and CBQCA) showed greater inconsistencies. In conclusion, protein quantification methods may not produce consistent accuracy among proteins translated from synonymously recoded sequences.

Monitoring and evaluation of immunogenicity is important for biological therapeutics due to the potential impact of antibodies on safety and efficacy. A comprehensive immunogenicity assessment was conducted to support clinical development of lonapegsomatropin, a prodrug of somatropin, administered once weekly. Lonapegsomatropin is approved in the United States for pediatric and adult growth hormone deficiency (GHD), and in other countries for pediatric GHD. Lonapegsomatropin consists of somatropin, an inert methoxy polyethylene glycol carrier (mPEG), and a proprietary TransCon® linker that transiently binds somatropin to the carrier. Four dedicated immunoassays were developed, validated, and applied in a risk-based tiered approach to rigorously assess the immunogenicity of lonapegsomatropin in children with GHD. Samples collected from 298 children with GHD treated with lonapegsomatropin for up to six years were analyzed for anti-drug antibodies (ADAs) against lonapegsomatropin (intact prodrug), released somatropin, and mPEG. Treatment-emergent anti-lonapegsomatropin, anti-somatropin, and anti-mPEG binding antibodies were detected in 4.7%, 5.4%, and 0.7% of treated children, respectively. ADA responses were low titer, transiently detected, and primarily observed within the first year of treatment, and no neutralizing antibodies were detected. These findings support the long-term low immunogenicity of lonapegsomatropin in children with GHD, consistent with somatropin. Importantly, the multi-assay tiered strategy applied here was robust and reproducible, enabling sensitive detection of ADAs against the intact prodrug, mPEG, and released somatropin across trials, and demonstrated its clinical applicability for evaluating a complex long-acting growth hormone like lonapegsomatropin.

Monoclonal antibodies (mAbs) play pivotal roles in modern biotherapeutics, offering targeted treatments for diseases such as cancers and autoimmune disorders. However, antibody self-association and propensity for aggregation can potentially compromise intended efficacy and pose safety concerns, necessitating robust analytical methods for aggregate detection and quantification. Traditional techniques like Size Exclusion Chromatography with Multi-angle Light Scattering (SEC-MALS) and analytical ultracentrifugation-sedimentation velocity (AUC-SV) are widely used but are limited by long analysis times, large sample requirements, and potential artifacts. Mass photometry (MP), a novel single-molecule technique, measures the interferometric scattering signal arising from molecules binding and unbinding to a glass slide. This study compares automated MP with SEC-MALS and AUC-SV for mAb aggregation analysis, demonstrating that MP equipped with automated sample handling delivers accurate and consistent analysis for the main and subpopulation species comparable to these established methods. Specifically, MP can not only determine the molecular mass distribution but also provide quantitative analysis of the monomer and High Molecular Mass Species (HMMS) generated with chemical cross-linking. When quantifying cross-linked HMMS of an IgG mAb, MP quantitation shows linear correlation with the SEC method, while MP provided superior resolution and dynamic range compared to SEC-MALS. Furthermore, MP and AUC-SV show comparable resolution in differentiating higher order aggregates and exhibit linear correlation for dimer quantitation, the primary form of mAb HMMS. With the capability to differentiate mAb monomers from various oligomeric forms, MP may represent a new approach that offers significant advantages in efficiency and throughput in quantification and characterization of mAb HMMS.

Signal-to-noise ratio (S/N) has emerged as a practical alternative to endpoint titer for evaluating anti-drug antibody (ADA) response magnitude. When titration is not performed, a scientifically justified threshold is needed to interpret longitudinal increases in S/N, particularly for identifying treatment-boosted ADA responses in subjects with pre-existing ADA. In conventional titer-based assessments, treatment-boosted responses are typically defined by an increase of two dilution steps relative to the pre-dose titer. In the absence of titer testing, an alternative threshold is needed to determine whether a post-dose/pre-dose increase in S/N exceeds expected analytical variability. This study evaluated minimum significant ratio (MSR) as a project-specific, analytical variability-based threshold for treatment-boosted ADA response classification using S/N. Two MSR calculation approaches were assessed across 11 validated clinical ADA assays representing diverse assay formats, therapeutic modalities, and analytical platforms. MSRPOS was derived from inter-assay precision data using positive controls, whereas MSRNEG was derived from ADA-negative individual samples and negative-control data generated during assay validation. Retrospective clinical ADA data from two projects were analyzed to compare longitudinal titer ratios with S/N ratios and evaluate treatment-boosted classifications using conventional titer-ratio and MSR-based S/N approaches. Longitudinal titer ratios and S/N ratios were strongly correlated, and MSR-based classifications were broadly consistent with conventional titer-ratio-based classifications. Compared with MSRPOS, MSRNEG showed higher positive percent agreement while maintaining high negative and overall percent agreement. Overall, these findings support MSR as a practical, project-specific, and statistically justified analytical framework for applying S/N to longitudinal treatment-boosted ADA response characterization.

Coamorphous systems are gaining interest as an effective formulation strategy to improve the solubility and dissolution of poorly water-soluble drugs. This study investigates the role of phenolic acids as coformers in enhancing the physicochemical properties of enzalutamide (ENZ), a poorly soluble anticancer drug. Five phenolic acids-p-coumaric acid (CMA), ferulic acid (FRA), cinnamic acid (CNA), vanillic acid (VNA), and p-hydroxybenzoic acid (HBA)-were selected based on their glass-forming ability and potential for molecular interactions. Coamorphous systems were prepared and characterized using powder X-ray diffraction (PXRD), modulated differential scanning calorimetry (mDSC), attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR), and Raman spectroscopy. All coformers successfully induced amorphization, evidenced by halo patterns in PXRD and single glass transition temperatures in mDSC. Spectroscopic analysis confirmed the absence of strong intermolecular bonding, indicating homogeneous mixing. The coamorphous systems exhibited improved solubility across physiological pH conditions and maintained supersaturation for at least four hours under non-sink dissolution testing. These results highlight the potential of phenolic acids as coformers in developing stable, high-performing coamorphous drug systems to enhance the bioavailability of poorly soluble APIs like enzalutamide.

Gamma Delta (γδ) T Cells are currently being evaluated as a therapeutic alternative to traditional alpha-beta (αβ) T-cells due to their superior safety profile and enhanced tissue retention properties. The application of CAR technology to gamma delta (γδ) T cells presents a novel therapeutic avenue with the potential to overcome some limitations of conventional CAR T-cell therapies, such as targeting solid tumors and reducing on-target, off-tumor toxicities. The objective of this manuscript is development of a translational PK-PD framework to first characterize in vitro killing potential of un-transduced and CAR transduced anti- CD20 Vδ1 γδ T cells as well as development of an in vivo mechanistic CK-PD model designed to understand the complex dynamics of CAR γδ T cells and their interaction with IL-15 and tumor cells. All the preclinical and clinical datasets along with relevant information were digitized and obtained from the published work on Adicet Bio's AD-001. The developed model was able to estimate the in vitro killing potential of untransduced and CAR transduced anti- CD20 Vδ1 γδ T cells as well as expansion, tissue distribution, the impact of lymphodepletion and interleukin-15 (IL-15), and the tumor-killing potential of CAR-modified γδ T cells. These insights offer a deeper understanding of the potential therapeutic benefits and mechanisms of γδ T cells in immunotherapy, particularly in their application against various cancers. The development of this translational framework can be paramount in understanding the underlying dose-exposure-response relationship of CAR modified γδ T cell therapy and facilitate the discovery and development of these agents.

Mycophenolate mofetil (MMF), a prodrug rapidly converted to mycophenolic acid (MPA), is widely used after lung transplantation, but fixed-dose therapy is complicated by exposure variability, enterohepatic recirculation, drug interactions, and limited routine use of area under the concentration-time curve (AUC)-guided therapeutic drug monitoring (TDM). Volumetric absorptive microsampling (VAMS) may support less invasive sampling, including later sampling windows relevant to enterohepatic recirculation. This prospective exploratory cohort study enrolled Taiwanese lung transplant recipients receiving MMF for more than three days. MPA concentrations were collected using VAMS at eight time points over a 12-hour dosing interval. Full-profile AUC0-12 was calculated, multiple linear regression was used to develop limited sampling strategies (LSS), and agreement between LSS-predicted and reference AUC values was assessed using Bland-Altman analysis. Exploratory population pharmacokinetic (popPK) modeling was used to characterize exposure variability. Nine patients were enrolled. MPA exposure was highly variable and generally below the commonly cited AUC0-12 target range of 30-60 mg·h/L (median AUC0-12: 18.11 mg·h/L; IQR: 14.33). Two- and three-point LSS models demonstrated strong predictive performance, particularly strategies incorporating both early absorption and later enterohepatic recirculation windows (C1 and C8). Leukopenia and infectious complications were common despite low total MPA exposure. Exploratory popPK analysis supported a one-compartment model with dual absorption pathways and identified patient-related factors associated with exposure variability. VAMS-based LSS may provide a practical approach for estimating MPA AUC0-12 in Taiwanese lung transplant recipients. These findings support simplified AUC-guided MMF TDM, while the exploratory popPK findings require validation in larger cohorts.

For approval of biosimilar drug products, the United States Food and Drug Administration (FDA) recommends a stepwise approach for obtaining the totality-of-the evidence for demonstration of the biosimilarity between a proposed biosimilar product and its reference (innovative) product. The stepwise approach consists of the assessment of analytical similarity, pharmacokinetics (PK) similarity or pharmacodynamics (PD) similarity, and clinical similarity, which may be conducted either sequentially or at the same time. Although the stepwise totality-of-evidence framework is scientifically rigorous, it may impose substantial time, cost, and operational burden on companies and may prolong the overall development and regulatory review timeline. These challenges can delay patient access to lower-cost biologic therapies. To improve efficiency while maintaining evidentiary rigor, under the Fundamental Biosimilarity Assumption that PK similarity is predictive of clinical similarity, we propose a biosimilar study utilizing an innovative two-stage adaptive trial design by combining a PK study and a clinical study. The proposed two-stage PK-clinical adaptive trial is efficient in the sense that (i) it allows decision-making at end of the first stage, (ii) it can avoid duplicated effort in demonstration of biosimilarity, (iii) it will shorten the development process, and most importantly (iv) it may reduce the development cost and may increase the probability of success in biosimilar drug development. Under the proposed two-stage PK-clinical adaptive trial design, a biosimilarity index based on the concept of reproducibility probability was derived for demonstration of biosimilarity. The biosimilarity index can also be applied for assessment of drug interchangeability under FDA's recommended switching designs.