AAPS Journal最新文献

Complex assays such as immunoassays can be affected by robustness and ruggedness factors. Associated risks can be reduced by a systematic performance assessment across key factors followed by control strategies. However, the large number of factors and their interactions can represent an experimental challenge. Statistical Design of Experiments (DOE) allows efficient evaluation of more factors with fewer total assay runs in addition to assessing potential factor interactions. We applied DOEs to the robustness evaluation of a vaccine potency ELISA. Test factors were selected based on a review and ranking of development data, scientific experience, and commonly expected sources of variability. Comparing different design options with 16-20 runs which was a laboratory limit, a 16-run Resolution III design was selected based on the total number of runs, the degree of factor confounding, and the potential projection properties. DOE data were first visually analyzed by plotting the concentration-responses of reference curves against DOE Runs followed by detailed statistical models of the maximum fluorescent curve signal and the WRMSE fit values. Initial confounding between factors and their interactions was reduced by eliminating factors with no impact from the models and by removing factors or interactions based on their likelihood of an impact after applying statistical and scientific expertise. Despite initial confounding, the designs allowed discerning an impact of plate manufacturer with interaction of coating concentration and time out of 15 factors with only 16 runs.

Protein aggregates and insoluble particles in biopharmaceutical products are impurities that can elicit immunogenicity. The protein aggregates and insoluble particles form during manufacturing and storage, and should be characterized to optimize the manufacturing process and establish a control strategy. Several issues regarding the evaluation and control of these particles have been concerned, and collaborative studies have been conducted in the Japan Biopharmaceutical Consortium to address them. However, there is still no consensus for utilizing analytical techniques in parallel to establish a control strategy for such protein aggregates and insoluble particles, which range in size from a few nanometers to several hundred micrometers. Therefore, in this study, we surveyed Japanese biopharmaceutical companies through a questionnaire including questions regarding analytical techniques used to establish control strategies for protein aggregates and insoluble particles at various development phases. To summary the survey results, we found that size exclusion chromatography, light obscuration, and visual inspection are consistently used from early development and formulation optimization stage to commercial manufacturing. Apart from the light obscuration method, flow imaging (FI) was the most commonly used technique for subvisible particle characterization; thus, the use of FI to establish a control strategy was documented. The recommendation for establishing a control strategy for protein aggregates and insoluble particles based on life-cycle of drug development are summarized.

The Blood-Brain Barrier (BBB) is a selective barrier between the Central Nervous System (CNS) and the peripheral system, regulating the distribution of molecules. BBB permeability has been crucial in CNS-targeting drug development, such as glioblastoma-related drug discovery. In addition, more CNS diseases still present significant challenges, for instance, neurological disorders like Alzheimer's Disease (AD) and drug abuse. Conversely, cannabinoid drugs that do not cross the BBB are needed to avoid off-target CNS psychotropic effects. In vitro and in vivo experiments measuring BBB permeability are costly and low throughput. Computational pharmacoanalytics modeling, particularly using deep-learning Graph Neural Networks (GNNs), offers a promising alternative. GNNs excel at capturing intricate relationships in graph-based information, such as small molecular structures. In this study, we developed GNNs model for BBB permeability using the graph representation of drugs. The GNNs were compared with other algorithms using molecular fingerprints or physical-chemical descriptors. With a dataset of 1924 molecules, the best GNNs model, a convolutional graph neural network using a normalized Laplacian matrix (GCN_2), achieved a precision of 0.94, recall of 0.96, F1 score of 0.95, and MCC score of 0.77. This outperformed other machine learning algorithms with molecular fingerprints. The findings indicate that the graphic representation of small molecules combined with GNNs architecture is powerful in predicting BBB permeability with high accuracy and recall. The developed GNNs model can be utilized in the initial screening stage for new drug development.

Tofacitinib is a potent, selective inhibitor of the Janus kinase (JAK) family of kinases with a high degree of selectivity within the human genome's set of protein kinases. Currently approved formulations for tofacitinib citrate are immediate release (IR) tablets, modified release (MR) tablets and IR solution. A once daily MR microsphere formulation was developed for pediatric patients. Previously, bioequivalence (BE) between the 10 mg once daily (QD) MR microsphere formulation and 5 mg twice daily (BID) IR solution has been established with PBPK virtual BE trials (VBE) in place of a clinical BE trial in healthy adult population. In this research, the PBPK model based VBE approach was extended to pediatric population. Pediatric PBPK model verification was conducted by first examining predicted vs observed demographic information such as body weight (BWT) and glomerular filtration rate (GFR). After confirming the alignment in demographic background between clinical study participants vs virtual pediatric subjects, multiple ontogeny profiles for CYP3A4 and CYP2C19 were examined. The established model predicted AUC and C_max within 1.5-fold of observed values for multiple trials, age groups and formulations. Lastly, VBE trials in pediatric subjects were conducted with PBPK model generated pharmacokinetic (PK) parameter values with clinically observed intra-subject coefficient of variation (ICV) in adults. Since ICV in pediatric population is unknown, the sensitivity around ICV was also evaluated to assess the BE risk between IR solution and MR microsphere formulation in pediatric population. The results demonstrated that the IR oral solution BID and MR microsphere formulation QD are BE in pediatric population.

This study investigates the role of FcRn in brain disposition of monoclonal antibodies. Human FcRn (hFcRn) expressing mice and different FcRn binding variants of a non-target binding antibody trastuzumab (WT) were used for the investigation. The FcRn binding mutations were: YTE, YPY, YQAY, and IHH. YQAY and YPY mutants have enhanced FcRn binding at both neutral and acidic pH (7+/6+). YTE mutant has enhanced FcRn binding at only acidic pH (7-/6+), and IHH mutant has no FcRn binding (7-/6-). The pharmacokinetics (PK) of these mutants in plasma, brain interstitial fluid (ISF), and brain homogenate were measured following intravenous administration. The area under the concentration-time curve (AUC) for all PK profiles and ratios of brain and plasma AUCs were calculated for comparison. Results showed that WT antibody had brain:plasma AUC ratio of 0.70% and ISF:plasma AUC ratio of 0.59%. Among all mutants, YPY exhibited the highest AUC ratio for brain (3.86%) and ISF (3.49%). YQAY had relatively high AUC ratios of 1.49% in the brain and 0.81% in ISF. YTE showed a similar AUC ratio in the brain (0.60%) and ISF (0.62%) compared to WT, while IHH exhibited similar AUC ratio in the brain (0.52%) but higher AUC ratio in ISF (2.48%). The results suggest that binding to FcRn at neutral and acidic pH facilitates transcytosis of antibody into the brain. Just increasing the binding to FcRn at acidic pH does not impact the disposition of antibody in the brain. Complete removal of FcRn binding might lead to prolonged retention of antibody in ISF. Together, these data demonstrate that FcRn significantly affects brain disposition of antibody, and engineering of Fc domain to alter the binding of antibody to FcRn may be exploited to achieve better exposure of antibodies in the brain.

Intestinal lymphatic absorption is a crucial alternative to portal uptake for highly lipophilic drugs (log P > 5), bypassing first-pass metabolism. Unlike the portal-hepatic pathway, lymphatic uptake is rarely considered in physiologically based pharmacokinetic (PBPK) models for oral delivery. Our study developed an innovative Gastro-Intestinal (GI)-lymph-PBPK model that includes GI absorption, chylomicron extraction (CE) to rescue drugs from gut extraction (GE), and bypass hepatic extraction (HE). This model introduces CE clearance (CL_CE), competing with GE clearance, to estimate the drug proportion subjected to CE versus GE. PBPK analysis for Buprenorphine revealed extensive GE (0.87) and HE (0.58), explaining the low bioavailability (F%) of 5.28% in rats. Buprenorphine prodrugs activated CL_CE, leading to CE ranging from 0.37 to 0.79, boosting oral F% to 39.9%-79.9% in rats. To translate from rat to human, our model considered species differences in GI transit time, formulation, food-dependent drug dissolution, allometric scaling in CL_CE, and between species variability in gut metabolism. Using Halofantrine, we established an allometric scaling factor for CL_CE at 1.1. Accounting for six times faster human gut metabolism, our model predicted an extremely low oral F% of 0.382% for Buprenorphine in humans. Incorporating the allometric scaled CL_CE competing with the extensive gut metabolism, our model predicted Buprenorphine prodrugs remains effective in enabling substantial absorption boosts, with oral F% estimates ranging from 15.8% to 56.7% in humans. This study highlights the significant potential of GI-lymph-PBPK modeling in predicting intestinal lymphatic absorption and facilitating cross-species translation.

Physical appearance (PA) is an attribute indicating the quality of parenteral pharmaceuticals. It is routinely evaluated during release and stability testing and included in regulatory filings. PA assessment of liquids involves three tests: visible particulates, clarity, and color. For each test, compendial general method chapters are available requiring minimal modification. This allows for a platform PA method approach, streamlining method readiness for new test articles. However, selecting the appropriate method is challenging, as no method suits all test articles, and pharmacopeias do not specify suitable condition(s) for each method. Improper method selection can lead to inappropriate specification setting and unreliable results. The need for guidance is especially urgent for vaccines, which often exhibit a wide range of PA attributes due to complex delivery systems and adjuvants that boost immunogenicity. This manuscript addresses this challenge by explaining method suitability and presenting a decision table for PA method selection based on the appearance properties of pharmaceuticals. A case study involving a yellow-turbid vaccine adjuvant is presented to demonstrate the practical application of the decision table. When color and turbidity make visual comparison to reference liquids difficult, instrumental clarity and visual qualitative methods are suitable options. The manuscript provides valuable insights on PA method selection and setting specifications for new parenteral pharmaceuticals. Furthermore, the decision table enables platform methods for test articles sharing similar appearance properties, eliminating the need for individual methods, reducing document preparation time for method and verification protocol, and enhancing the consistency and efficiency of GMP testing for PA.

The drug titration paradox arises when higher drug concentrations are paradoxically associated with poorer efficacy outcomes, due to the titration of an individual's drug dose to achieve a desired effect. In cases with substantial intraindividual variability of the disease state, the drug titration paradox can also occur on the individual level (resulting in a higher dose when the individual has a worse disease state) and it has been suggested that it may not be possible to estimate the true exposure-response (ER) relationship in such situations. We simulated a titration study with strong intra-individual variability of disease state (causing the drug titration paradox at the individual level) and investigated the performance of four PKPD modelling methods in obtaining an unbiased estimate of the ER relationship. Strong bias in the estimated ER relationship was observed with two commonly used modelling methods: the model which only estimated inter-individual variability (IIV) and the model that included IIV and inter-occasion variability (IOV) on disease severity. In contrast, inclusion of stochastic differential equations (SDE) or accounting for the autocorrelation of the residual error between observations did yield successful estimation of the ER relationship without bias. The success of these methods can be understood from the principles of causal inference: confounding is avoided by controlling for the previous observations which drive the drug titration. Our results underline the importance of adequately characterizing intra-individual variability to avoid bias in PKPD modelling, especially for clinical areas where titration designs are common, such as analgesia.

Friedreich's ataxia is a rare, progressive, genetic disorder, the root cause of which is a significant deficiency in the mitochondrial protein frataxin. Frataxin is ubiquitously expressed, but its deficiency results in a variety of debilitating symptoms, with disease severity, rate of progression and age of onset inversely correlating with tissue frataxin levels. Nomlabofusp is a novel cell penetrant peptide based recombinant fusion protein designed to enter cells and deliver human FXN into the mitochondria. Using immunofluorescence staining and western blot we show that frataxin delivered by nomlabofusp is detected in the mitochondria of H9c2 and SH-SY5Y cells. Also in these cells, and in C2C12 and HEK293 cells, we demonstrate the presence of mature frataxin after nomlabofusp exposure. Finally, using buccal swab tissue samples taken from study subjects in a Phase 1 clinical trial who received nomlabofusp, we show increases in mature frataxin levels along with marked changes in gene expression post-administration suggesting intracellular pharmacodynamic activity. Together, these results demonstrate that nomlabofusp enters the cell and localizes to the mitochondria, releasing mature frataxin that appears to be biologically active and support the use of nomlabofusp as a potential treatment for patients with Friedreich's ataxia.

Cancer immunotherapy is poised to be one of the major modalities for cancer treatment. Messenger RNA (mRNA) has emerged as a versatile and promising platform for the development of effective cancer immunotherapy. Delivery systems for mRNA therapeutics are pivotal for their optimal therapeutic efficacy and minimal adverse side effects. Lipid nanoparticles (LNPs) have demonstrated a great success for mRNA delivery. Numerous LNPs have been designed and optimized to enhance mRNA stability, facilitate transfection, and ensure intracellular delivery for subsequent processing. Nevertheless, challenges remain to, for example, improve the efficiency of endosomal escape and passive targeting. This review highlights key advancements in the development of mRNA LNPs for cancer immunotherapy. We delve into the design of LNPs for mRNA delivery, encompassing the chemical structures, characterization, and structure-activity relationships (SAR) of LNP compositions. We discuss the key factors influencing the transfection efficiency, passive targeting, and tropism of mRNA-loaded LNPs. We also review the preclinical and clinical applications of mRNA LNPs in cancer immunotherapy. This review can enhance our understanding in the design and application of LNPs for mRNA delivery in cancer immunotherapy.