AAPS Journal最新文献

Raman spectroscopy is a proven Process Analytical Technology (PAT) for monitoring mammalian cell culture processes in biopharmaceutical manufacturing. In-line Raman probes provide real-time chemical fingerprints of metabolites and cell culture health analyzed via chemometric modeling. However, Raman hardware (e.g. cables, detectors, optics, probes, and lasers) and software - performing calibration, noise reduction, and cosmic ray removal - impart vendor specific spectral signatures, rendering Raman chemometric models specific to the vendors. This vendor specificity complicates method validation and transfer between manufacturing sites deploying different vendor equipment and impedes upgrades, maintenance, and replacement of obsolete Raman equipment. In this work, we compared two calibration transfer methods to address vendor-to-vendor variation. Piecewise Direct Standardization (PDS) and Spectral Subspace Transformation (SST) methods successfully reduced spectral response variation between previous (Parent) and new (Child) Raman systems. We tested calibration transfer results with offline samples and an established validation approach utilizing paired spectra from Parent and Child Raman systems. Finally, we explored the influence of calibration transfer parameters including training set size, preprocessing position, and window size (number of components) for PDS and SST. Through this investigation we demonstrate the feasibility of this proposed vendor-to-vendor calibration transfer approach as a promising and effective chemometric model transfer between Raman vendors without significant method re-development or re-validation. This approach improves agility within the deployment strategy of chemometric models across supply chain networks and bolsters Raman spectroscopy as a versatile PAT tool for advanced manufacturing within the biopharmaceutical industry.

Here we challenge the assumption that hepatic uptake can be rate-limited solely by transporter-mediated influx clearance, as commonly concluded in interpretations based on the Extended Clearance Concept (ECC). We initially review the derivation of renal and hepatic clearance independent of differential equations based on adaptation of Kirchhoff's Laws from physics, incorporating clinically relevant aspects such as transporter activity, organ blood flow, and clearance from the site of drug delivery into systemic circulation. In doing so, we highlight the limitations of the ECC framework, which does not adequately capture all aspects of these mechanistic elements and note that no experimental data or PBPK analyses definitively support its validity. In contrast, we show that all hepatic clearance data can be adequately explained based on our derivations, which provide a more robust and mechanistically consistent framework for interpreting renal and hepatic clearance. The derived equations define the net difference between influx and efflux clearances as the key determinant of transporter involvement in hepatic drug disposition, rather than considering influx alone as in the ECC. This approach is analogous to the treatment of secretion and reabsorption clearances as opposing processes in renal clearance, where their difference determines the net transport. We also question the mechanistic accuracy of determining hepatic influx and efflux clearances in vitro using initial rates of membrane passage, as is typically done for passive processes. Such measurements inherently reflect the net intramembrane difference between influx and efflux clearances, and do not allow for independent quantification of directional transport, due to the inability to measure drug concentrations within the membrane. Finally, while we acknowledge the utility of ECC and PBPK analyses for predicting changes in pharmacokinetic exposure due to DDIs (or other variables such as disease state, pharmacogenomics, etc.), we caution that model-based data fitting, however useful, does not constitute mechanistic validation.

The administration of therapeutic proteins, such as monoclonal antibodies (mAbs) may result in the formation of anti-drug antibodies (ADA), but there is limited publicly available data for biotherapeutics with exogenous targets or with targets in low abundance systemically. ADAs can impact a biotherapeutic's safety, efficacy, and pharmacokinetics, so evaluation of immunogenicity is required for all therapeutic proteins. This analysis sought to evaluate the immunogenicity of two mAbs against COVID-19, casirivimab (CAS) and imdevimab (IMD) administered as a cocktail (CAS + IMD), in multiple clinical trials across different participant populations to add knowledge about the immunogenicity risk of protein therapeutics against exogenous targets and the association between immunogenicity and drug exposure. Overall, the incidence of treatment-boosted and treatment-emergent ADAs and neutralizing antibodies in both disease treatment and prophylaxis settings were low (i.e., < 11%). Participants in these studies were followed for up to 225 days post-last dose. Over time, the incidence of treatment-emergent and treatment-boosted immunogenicity for each mAb increased, but the rates and titers remained low overall; when it developed, patients usually developed ADA to both mAbs. Frequency of administration, dose, and route of administration did not appear to impact rates of immunogenicity. Concentrations of CAS and IMD were similar between participants of different immunogenicity status, indicating immunogenicity of CAS and IMD had no impact on pharmacokinetics of these mAbs; there was also no observed impact on safety or efficacy. The data presented here may provide supportive information for assessing the immunogenicity risk of protein therapeutics with exogenous targets.

Immunogenicity, commonly manifested through the development of anti-drug antibodies (ADAs), is a critical concern for therapeutic antibodies as it can adversely affect pharmacokinetics (PK), efficacy, and safety. Although ADA-related impacts have been closely evaluated for individual antibody drugs, cross-product analyses of approved antibody drugs remain limited. In this study, we examined 92 antibody therapeutics approved by the Japanese regulatory authority as of March 2024, focusing on their review reports and prescribing information, to quantitatively assess the actual clinical impacts of ADA. Among these drugs, ADA-related decreased drug exposure, reduced efficacy, and worsened safety were indicated in the prescribing information of 18.5%, 10.9%, and 5.4%, respectively. Interestingly, ADA-related descriptions of both decreased drug exposure and reduced efficacy were observed in significantly lower proportions among oncology drugs than among non-oncology drugs. Furthermore, humanized antibodies exhibited higher proportions of ADA-related impacts than human antibodies; in particular, decreased exposure was significantly more common with humanized antibodies (27.7%) than with human antibodies (8.6%), supporting the concept that human antibodies have lower immunogenic potential. In addition, our evaluation of review reports indicated that all drugs with ADA-related reduced efficacy had decreased exposure. To the best of our knowledge, this is the first study to quantitatively evaluate the clinical impact of immunogenicity in approved antibody therapeutics in Japan. These results are expected to inform the appropriate clinical use of antibody therapeutics and contribute to a more comprehensive understanding of immunogenicity risk assessments during clinical development.

Short contacts between medical devices and medications that they contain can cause drug sorption or leaching of unwanted compounds which can have clinical consequences. However, the parameters that govern these interactions are still incompletely understood. The objectives of this study were to evaluate the impact of the formulation parameters on migration of 2,4 Di-tert-butylphenol (2,4 DTBP) and sorption of tacrolimus (TAC) in a model micellar ophthalmic drug formulation which is known to interact with silicone elastomers. A design of experiments was established to evaluate the impact of the formulation parameters on 2,4 DTBP leaching and tacrolimus sorption by varying the initial concentrations of TAC (0.04; 0.2 and 1 mg/mL), polyoxyethylenated castor oil (KEL) (32; 80 and 200 mg/mL) and ethanol absolute (EtOH) (10 and 100 mg/mL), at 5°C and 25°C, during static contact between various formulations and silicone valves obtained from an ophthalmic multidose delivery device. Concentrations of 2,4 DTBP and TAC were monitored by liquid chromatography coupled with an UV-visible detector. Under the conditions studied, the amount of 2,4 DTBP leaching out increased during contact with the silicone valve, with increasing TAC concentration being quite interestingly the main factor decreasing its ability to leach out, whilst ethanol increased its leachability. The silicone valve did not cause any significant decrease of TAC concentrations over the duration of the study, regardless of the concentrations of KEL, EtOH, or temperature. This study showed the impact of formulation on the leaching of 2,4 DTBP from a silicone component.

Formation of anti-drug antibodies (ADA) and, in particular, neutralizing antibodies (NAb), is a major risk to the development of biotherapeutics. Cell-based assays, which can reflect the mechanistic interactions among the drug, target, and NAb, are often most suitable for the detection of NAb. We report the development and validation of cell-based platform assays for a class of incretin molecules. An affinity capture elution step was employed to improve drug tolerance of a cell-based cyclic adenosine monophosphate (cAMP) assay. Assay conditions and procedures, including acid elution, cell density, and TAG labeled cAMP (cAMP-TAG) incubation time and concentration, were thoroughly optimized. Delta percent (Δ%) Neutralization was used to reduce assay variability during validation and sample analysis. This platform method has been applied to several incretin molecules and has demonstrated desired sensitivity, drug tolerance, and robustness.

A special panel session at the 2024 NCS Conference in Wiesbaden, Germany, brought together experts to explore the evolving landscape of patient-centric drug product development and specifications. Traditional specifications often rely on manufacturing variability and phase 3 clinical trial data. However, patient-centric specifications shift the focus toward direct clinical relevance, establishing customer-driven acceptance criteria. Discussions covered innovative approaches to defining patient-centric specifications, leveraging prior knowledge, biopharmaceutics, formulation science, regulatory input, and statistical methodologies. The session also highlighted efficient QbD studies, enabling the linkage of Critical Process Parameters (CPPs) and Critical Material Attributes (CMAs) to Chemistry, Manufacturing and Controls (CMC) Critical Quality Attributes (CQAs). While these approaches hold great promise, challenges remain such as cross-disciplinary collaboration, data integration, and the development of robust databases to inform future drug and biologicals development. As industry experience grows, these efforts will pave the way for continuous improvement in nonclinical-clinical linkage designs, enhancing patient outcomes.

QSP models are increasingly being applied to drug development. Calibrating virtual populations (VPops) to clinical data helps explore patient variability, study populations of interest, and predict clinical outcomes for new therapies. In previous work, we have developed the QSP Toolbox, a library of tools and workflows for developing VPops. The aim is to create a VPop that fits clinical data from a variety of sources and with a variety of data types, allowing for differences in the quality of the data. The goodness-of-fit (GOF) of the VPop is given by a scalar function $p$ whose form is inspired by the p-value of Fisher's combined hypothesis test. However, our VPop development workflow has been hampered by the need to perform repeated optimization of "prevalence weights" to maximize $p$ . In this article, we describe how we can speed up our workflow by using proxy GOF functions whose maximization can be reduced to the relatively easy problem of minimizing a convex quadratic objective function. A successful run of this "proxy-guided" workflow generates a VPop with very good $p$ -fit, but without having to continually maximize $p$ throughout the development process. We find that our proxy-guided workflow can calibrate VPops much faster than the original ( $p$ -guided) workflow.

Chronic human immunodeficiency virus (HIV) infection continues to pose a major global health challenge, with an estimated 39.9 M cases and 1.3 M new infections per year. Up to 75%, of people with HIV (PWH) are currently being treated with antiretroviral therapy. The development of effective treatments for HIV requires a detailed understanding of the virus-host interaction. Often, mathematical models are used to inform this understanding as well as to characterize therapeutic response. This work aims to develop a single and fully identifiable population semi-mechanistic pharmacokinetic/pharmacodynamic (popPKPD) model integrating data (drug levels, CD4 + T cells and viral RNA) of five different antiretroviral (ARV) drugs administered in monotherapy: lenacapavir, bictegravir, tenofovir alafenamide, emtricitabine and elvitegravir. Data were obtained from six phase 1 clinical studies and model development was performed using NONMEM 7.5.1. The viral dynamics were best described by expanding the "basic" viral dynamics model with two more populations of infected cells: chronically and latently infected. Obtained death rates values of infected cells & virus were consistent with literature values. Population baseline HIV-RNA was estimated to be 43,460 copies/mL, with an IIV of 141%, the estimated baseline concentrations of CD4 + cells varied largely across the different cell types in the model: 410, 20, 7 and 7 cells/mL for uninfected, infected, chronic and latently infected cells, respectively. The popPKPD model was able to capture key aspects of viral dynamics, drug behaviour, and treatment response. The models developed will serve as a useful tool for further development and optimization of these HIV therapies, especially when evaluating multiple ARVs to be administered in a novel combination setting.

Using pharmacometrics to inform drug discovery and development decisions requires effective communication of models and data to all stakeholders. The new "vachette" visualization method presented here enables modelers and non-modelers to see how a model integrates and represents all data across relevant subgroups, showing how the model "sees" the data when it accounts for covariates. Vachette starts with user-provided model simulations ("curves"), together with observations used in creating (or otherwise relevant to) the model. It automatically produces a single, intuitive plot overlaying all observations onto a user-selected reference curve, accounting for covariate effects and preserving remaining random effects. The method automatically identifies characteristic landmarks (e.g., minima, maxima, and inflection points) which are used to split each curve into segments. A transformation on both x- and y-axes is then applied to each segment and its corresponding observations, accounting for covariate effects by aligning the segments to the reference, allowing intuitive visualization in one plot of covariate effects and of model fit to the data, preserving the distance between model predictions and the observations. Vachette-transformed data can also be used to enhance the utility of model assessments such as visual predictive checks and residual plots. Model visualizations using vachette enable easier and more effective evaluation and communication of the pharmacometric results critical to informing key decisions. Here the vachette method is described and its utility and flexibility are demonstrated through application to multiple types of pharmacometrics models, suggesting that vachette is a useful addition to the pharmacometrician's toolbox.