AAPS Journal最新文献

Biotherapeutic products have revolutionized the diagnosis and treatment of numerous complex to manage diseases, such as cancer, autoimmune disorders, and infectious diseases. When compared with small molecules (pharmaceuticals), this class of therapeutic agents has proven to be more effective and, in general, have fewer side effects. However, they are more expensive (typically 10-1000 times more) and this limits their global affordability and accessibility. This realization has driven the rise of biosimilars, which are products that have been deemed to be highly similar to a reference product that has been already approved in the regulatory jurisdiction under consideration. A key element in receiving approval for a biosimilar product is for the manufacturer to successfully demonstrate its biosimilarity, an exercise comprising extensive characterization using a gamut of orthogonal, high-resolution analytical and functional tools. Once biosimilarity has been demonstrated, the extent of clinical evaluation that the manufacturer has to perform is significantly curtailed. In this white paper, we discuss various challenges that a manufacturer faces when performing these biosimilarity exercises for monoclonal antibody (mAb) products. Particular attention has been paid to the impact of excipients on these assessments. Using multiple case studies, we elucidate the impact of the removal of these excipients on product stability as well as the impact of their presence on the outcome from any of the analytical and functional tools that are commonly used in these biosimilarity assessments. We also offer best practices for the manufacturers of biosimilars to overcome the challenges associated with the presence of excipients in mAb formulations. Finally, we discuss the number of batches, the development of acceptance criteria and the importance of orthogonal methods in the demonstration of biosimilarity using examples from regulatory filings and guidances.

On Oct 7 and 8, 2024, the US Food and Drug Administration (FDA) and the Center for Research on Complex Generics (CRCG) co-hosted a workshop titled "Scientific and Regulatory Considerations for Assessment of Immunogenicity Risk for Generic Peptide and Oligonucleotide Drug Products". Stakeholders from the FDA, industry, academia, and contract research organizations convened to discuss strategies for advancing risk assessment methodologies and regulatory frameworks for complex generic products. By assembling experts from various sectors, the workshop explored various available strategies for immunogenicity risk assessment, providing valuable insights to support the development and assessment of generic peptide and oligonucleotide drug products. The discussions fostered a deeper understanding of how these methodologies can inform regulatory decision-making and enhance the development of safer and more effective therapeutics.

A growing number of chimeric antigen receptor (CAR) T-cell therapies have been developed and investigated in clinical studies, with several FDA-approved therapies targeting CD19 and BCMA antigens on hematological malignancies. To further expand the indication to solid tumors and autoimmune diseases, a new generation of CAR T-cell therapies has been investigated to explore new tumor-associated antigens, allogeneic options, and cytokine armoring strategies, etc. to overcome the current limitations, including safety concerns, relapse rates, tumor microenvironment challenges, patient accessibility and manufacturing complexities. Cellular kinetics and biodistribution assessments are crucial in understanding the efficacy and safety of cell therapy, as a living drug that typically exhibits four distinct phases: distribution, expansion, contraction, and persistence within the body. Droplet digital PCR has emerged as the technology of choice in cell and gene therapy for transgene quantification with higher sensitivity, specificity, reproducibility, and absolute quantification, and as an end-point PCR provides higher tolerance to PCR inhibition. In the meantime, several unique challenges remain to be addressed in the cellular kinetics and biodistribution studies of CAR T, depending on the program stage, types of immune cells, and target indication (hematological and solid tumors, autoimmune). This opinion paper discusses the challenges and considerations of PCR-based cellular kinetics and biodistribution assessment to support emerging adoptive cell therapy programs.

Physiologically based pharmacokinetic (PBPK) modeling is a mathematical method for predicting drug pharmacokinetics based on individual physiological information, and its usefulness has been attracting attention in recent years. This study aimed to investigate and compare the extent to which PBPK models are utilized in drug package inserts in Japan, the United States, and Europe. We searched the official websites of the PMDA, FDA, and EMA to examine the number and content of drugs that mention the use of PBPK modeling as of 2024. Furthermore, we compared the consistency and detail of the descriptions between Japan, the United States, and Europe. As a result, 38 drugs in Japan listed PBPK modeling, many of which were related to drug-drug interactions. Compared with Europe, there were no complete matches and only seven partial matches, while compared with the United States, there were two complete matches and 24 partial matches. A comparison of the details of the 24 partial matches revealed a tendency for Japanese package inserts to be more detailed. PBPK model analysis has become increasingly important in recent drug development, and Japan in particular has a tendency to provide more detailed information on package inserts. This study suggests the potential for further utilization of PBPK models in the field of clinical pharmacology.

Cross-validating bioanalytical assays between laboratories is challenging, and antidrug antibody (ADA) assays are particularly difficult to cross-validate due to the lack of regulatory guidance specific to this topic. As a result, different companies have adopted varying approaches. Currently, bioanalytical practices for ADA assay cross-validation are not harmonized. Therefore, it is important to share individual company experiences and practices to facilitate harmonization for industry standards. This manuscript presents Boehringer Ingelheim's strategy and practice, referred to as a "data-driven stepwise approach," for clinical ADA assay cross-validation. In Step 1, the ADA assay is first validated at the reference laboratory and then qualified at the comparator laboratory. In Step 2, both laboratories demonstrate comparable responses through cut-point evaluation, confirmed by statistical analysis. In Step 3, identical blinded test samples are analyzed at both laboratories to confirm consistent classification of results as ADA-negative or ADA-positive. Upon satisfactory completion of these steps, the ADA assay is considered successfully cross-validated from the reference laboratory to the comparator laboratory. Case studies are also presented.

Accurate assessment of tissue distribution for oligonucleotide therapeutic (ONT) drug candidates is essential for understanding pharmacokinetic behavior and predicting therapeutic efficacy. ONTs present a unique challenge with their rapid systemic clearance coupled with prolonged tissue retention, making comprehensive tissue concentration evaluation critical for successful drug development. The IQ Consortium Tissue Concentration Working Group surveyed member companies about their current tissue concentration assessment methods to understand industry practices and identify areas for improvement. Most companies reported that ONTs still represent a relatively small portion of their pre-candidate selection portfolios, reflecting the evolving nature of this therapeutic modality. siRNAs dominated development efforts across surveyed organizations, followed by antisense oligonucleotides, indicating clear therapeutic class preferences within the industry. Assessment strategies varied considerably across organizations, highlighting different approaches to resource allocation and risk management. While some companies routinely evaluate tissue concentrations for all ONT programs regardless of indication or target, others take a more selective, program-dependent approach based on compound characteristics and therapeutic objectives. Despite this strategic variability, there was universal reliance on LC-MS for quantification, often supplemented with qPCR/RT-qPCR and hybridization assays for comprehensive analytical coverage. All surveyed companies integrate tissue concentration data into translational pharmacokinetic modeling efforts, yet few have adopted physiologically-based pharmacokinetic (PBPK) models as standard practice. Companies recognize the value of improving ONT tissue distribution assessment through standardized methodology tailored to specific oligonucleotide classes.

COVID-19 disease outcomes can vary considerably among infected patients. Most studies have focused on patients with severe COVID-19. However, investigations of asymptomatic infection can provide insights into patient-specific immunological features that protect patients from COVID-19 symptoms. Recent studies have shown an association between common human leukocyte antigen (HLA) alleles and asymptomatic COVID-19 infections. Here we utilize machine learning in conjunction with explainable AI (XAI) to identify alleles in five HLA loci that can be either protective or put the patient at risk for symptomatic COVID-19. Data from the public online HLA-COVID database (1946 samples) was used for training and validating multiple ML classification models to identify the top performing model. The model was then further processed with XAI via SHAP (SHapley Additive exPlanations) to identify the protective and high-risk HLA alleles. This study provides a proof-of-concept study for utilizing machine learning to provide valuable insights for COVID-19 patients. These findings can be translated into clinical algorithms to help physicians personalize COVID-19 treatments and achieve better clinical outcomes.

Immunogenicity testing for anti-drug antibodies (ADAs) is crucial in therapeutic protein development, yet current quasi-quantitative assays struggle to accurately measure ADAs when the antibodies have different binding strength (affinities) or due to heterogeneity of ADAs and residual drug interference. While traditional QC-based assay development is limited by the lack of representative ADA reference standards, we propose Model-Informed Assay Development (MIAD) as a transformative solution. MIAD mathematically simulates complex analyte-reagent interactions to identify optimal conditions for signal-generating analyte-reagent complex (ARC) formation, enabling scientifically sound assay optimization independent of positive controls. Our findings demonstrate that optimal sample dilution and reagent concentrations can overcome drug interference and improved detection of antibodies (ADAs) with different binding strengths. This work applies MIAD to address critical ADA assay challenges: drug tolerance and affinity-dependent detectability. We tested MIAD's prediction in three real world case studies and found strong agreement. Our findings show that optimized sample dilutions and reagent concentrations effectively overcome drug interference and affinity differences, enhancing ADA detectability and recovery. MIAD also helps understanding whether a hook-shaped curve is due to a prozone effect or drug interference, guiding the development of unbiased assays crucial for accurate S/N-based magnitude estimation.

Pimozide and PSSI-51 are under study for their potential glucose-lowering effects in type 2 diabetes, through their abilities to inhibit succinyl-CoA:3-ketoacid CoA transferase, the rate-limiting enzyme of ketone oxidation. To understand their pharmacokinetics, they were administered to Sprague Dawley male and female rats after standard and high-fat diets. Initially five rats each were given 10 mg/kg of each agent orally, and using serial blood withdrawals from jugular vein cannulas, the blood samples were assayed for drug and basic pharmacokinetic data estimated using compartmental analysis. A separate group of male and female rats were given the same single dose after either 10 (pimozide) or 13 (PSSI-51) weeks of feeding with a standard or high-fat diet, followed by two blood samples after each dose from the saphenous vein. Bayesian forecasting in conjunction with the mean and variance of pharmacokinetic parameters and assay coefficient of variation, was used to estimate the pharmacokinetic parameters in these rats. The two drugs differed in their optimal pharmacokinetic model (pimozide one compartment, PSSI-51 two compartment). Both drugs possessed a high volume of distribution (Vd/F), but the oral clearance (CL/F) of PSSI-51 was much higher than that of pimozide, in line with earlier observations using rat microsomal experiments. The high-fat diet significantly reduced the oral CL and Vd of PMZ in both male and female rats, whereas no such effect was observed for PSSI-51.