AAPS Journal最新文献

Tyrosine kinase inhibitors (TKIs) have demonstrated significant efficacy against various types of cancers through molecular targeting mechanisms. Over the past 22 years, more than 100 TKIs have been approved for the treatment of various types of cancer indicating the significant progress achieved in this research area. Despite having significant efficacy and ability to target multiple pathways, TKIs administration is associated with challenges. There are reported inconsistencies between observed food effect and labeling administration, challenges of concomitant administration with acid-reducing agents (ARA), pill burden and dosing frequency. In this context, the objective of present review is to visit administration challenges of TKIs and effective ways to tackle them. We have gathered data of 94 TKIs approved in between 2000 and 2022 with respect to food effect, ARA impact, administration schemes (food and PPI restrictions), number of pills per day and administration frequency. Further, trend analysis has been performed to identify inconsistencies in the labeling with respect to observed food effect, molecules exhibiting ARA impact, in order to identify solutions to remove these restrictions through novel formulation approaches. Additionally, opportunities to reduce number of pills per day and dosing frequency for better patient compliance were suggested using innovative formulation interventions. Finally, utility of physiologically based pharmacokinetic modeling (PBPK) for rationale formulation development was discussed with literature reported examples. Overall, this review can act as a ready-to-use-guide for the formulation, biopharmaceutics scientists and medical oncologists to identify opportunities for innovation for TKIs.

Gefapixant is a weakly basic drug which has been formulated as an immediate release tablet for oral administration. A physiologically based biopharmaceutics model (PBBM) was developed based on gefapixant physicochemical properties and clinical pharmacokinetics to aid formulation selection, bioequivalence safe space assessment and dissolution specification settings. In vitro dissolution profiles of different free base and citrate salt formulations were used as an input to the model. The model was validated against the results of independent studies, which included a bioequivalence and a relative bioavailability study, as well as a human ADME study, all meeting acceptance criteria of prediction errors ≤ 20% for both Cmax and AUC.  PBBM was also applied to evaluate gastric pH-mediated drug-drug-interaction potential with co-administration of a proton pump inhibitor (PPI), omeprazole. Model results showed good agreement with clinical data in which omeprazole lowered gefapixant exposure for the free base formulation but did not significantly alter gefapixant pharmacokinetics for the citrate based commercial drug product. An extended virtual dissolution bioequivalence safe space was established.  Gefapixant drug product batches are anticipated to be bioequivalent with the clinical reference batch when their dissolution is > 80% in 60 minutes. PBBM established a wide dissolution bioequivalence space as part of assuring product quality.

Synthetic cathinones represent one of the largest and most abused new psychoactive substance classes, and have been involved in numerous intoxications and fatalities worldwide. Methcathinone analogues like 3-methylmethcathinone (3-MMC), 3-chloromethcathinone (3-CMC), and 4-CMC currently constitute most of synthetic cathinone seizures in Europe. Documenting their consumption in clinical/forensic casework is therefore essential to tackle this trend. Targeting metabolite markers is a go-to to document consumption in analytical toxicology, and metabolite profiling is crucial to support investigations. We sought to identify 3-CMC, 4-CMC, and 4-bromomethcathinone (4-BMC) human metabolites. The substances were incubated with human hepatocytes; incubates were screened by liquid chromatography-high-resolution tandem mass spectrometry and data were mined with Compound Discoverer (Themo Scientific). 3-CMC-positive blood, urine, and oral fluid and 4-CMC-positive urine and saliva from clinical/forensic casework were analyzed. Analyses were supported by metabolite predictions with GLORYx freeware. Twelve, ten, and ten metabolites were identified for 3-CMC, 4-CMC, and 4-BMC, respectively, with similar transformations occurring for the three cathinones. Major reactions included ketoreduction and N-demethylation. Surprisingly, predominant metabolites were produced by combination of N-demethylation and ω-carboxylation (main metabolite in 3-CMC-positive urine), and combination of β-ketoreduction, oxidative deamination, and O-glucuronidation (main metabolite in 4-CMC-positive urine). These latter metabolites were detected in negative-ionization mode only and their non-conjugated form was not detected after glucuronide hydrolysis; this metabolic pathway was never reported for any methcathinone analogue susceptible to undergo the same transformations. These results support the need for comprehensive screening strategies in metabolite identification studies, to avoid overlooking significant metabolites and major markers of consumption.

Addressing the intertwined challenges of antimicrobial resistance and impaired wound healing in diabetic patients, an oil/water emulsion-based nano-ointment integrating phenylpropanoids-Eugenol and Cinnamaldehyde-with positively-charged silver nanoparticles was synthesized. The process began with the synthesis and characterization of nano-silver, aimed at ensuring the effectiveness and safety of the nanoparticles in biological applications. Subsequent experiments determined the minimum inhibitory concentration (MIC) against pathogens such as Streptococcus aureus, Pseudomonas aeruginosa and Candida albicans. These MIC values of all three active leads guided the strategic formulation of an ointment base, which effectively integrated the bioactive components. Evaluations of this nano-ointment revealed enhanced antimicrobial activity against both clinical and reference bacterial strains and it maintained stability after freeze-thaw cycles. Furthermore, the ointment demonstrated superior in-vitro diabetic wound healing capabilities and significantly promoted angiogenesis, as shown by enhanced blood vessel formation in the Chorioallantoic Membrane assay. These findings underscore the formulation's therapeutic potential, marking a significant advance in the use of nanotechnology for topical wound care.

Bispecific and multispecific agents have become increasingly utilized in cancer treatment and immunotherapy, yet their complex design parameters present a challenge in developing successful therapeutics. Bispecifics that crosslink receptors on two opposing cells can provide specific activation of a receptor only when these cells are in close spatial proximity, such as an immune cell and cancer cell in a tumor. These agents, including T cell activating bispecifics, can avoid off-tumor toxicity through activation only in the tumor microenvironment by utilizing a tumor target to cluster T-cell receptors for a selective costimulatory signal. Here, we investigate a panel of PD-1/CD137 targeted Humabody V_H domains to determine the key factors for T cell activation, such as affinity, valency, expression level, domain orientation, and epitope location. Target expression is a dominant factor determining both specificity and potency of T cell activation. Given an intrinsic expression level, the affinity can be tuned to modulate the level of activation and IC₅₀ and achieve specificity between low and high expression levels. Changing the epitope location and linker length showed minor improvements to activation at low expression levels, but increasing the valency for the target decreased activation at all expression levels. By combining non-overlapping epitopes for the target, we achieved higher receptor activation at low expression levels. A kinetic model was able to capture these trends, offering support for the mechanistic interpretation. This work provides a framework to quantify factors for T cell activation by cell-crosslinking bispecific agents and guiding principles for the design of new agents.

The recruitment of a parallel, healthy participants (HPs) arm in renal and hepatic impairment (RI and HI) studies is a common strategy to assess differences in pharmacokinetics. Limitations in this approach include the underpowered estimate of exposure differences and the use of the drug in a population for which there is no benefit. Recently, a method was published by Purohit et. al. (2023) that leveraged prior population pharmacokinetic (PopPK) modeling-based simulation to infer the distribution of exposure ratios between the RI/HI arms and HPs. The approach was successful, but it was a single example with a robust model having several iterations of development and fitting to extensive HP data. To test in more studies and models at different stages of development, our catalogue of RI/HI studies was searched, and those with suitable properties and from programs with available models were analyzed with the simulation approach. There were 9 studies included in the analysis. Most studies were associated with models that would have been available at the time (ATT) of the study, and all had a current, final model. For 3 studies, the HP PK was not predicted well by the ATT (2) or final (1) models. In comparison to conventional analysis of variance (ANOVA), the simulation approach provided similar point estimates and confidence intervals of exposure ratios. This PopPK based approach can be considered as a method of choice in situations where the simulation of HP data would not be an extrapolation, and when no other complicating factors are present.

Stepwise covariate modeling (SCM) has a high computational burden and can select the wrong covariates. Machine learning (ML) has been proposed as a screening tool to improve the efficiency of covariate selection, but little is known about how to apply ML on actual clinical data. First, we simulated datasets based on clinical data to compare the performance of various ML and traditional pharmacometrics (PMX) techniques with and without accounting for highly-correlated covariates. This simulation step identified the ML algorithm and the number of top covariates to select when using the actual clinical data. A previously developed desipramine population-pharmacokinetic model was used to simulate virtual subjects. Fifteen covariates were considered with four having an effect included. Based on the F1 score (an accuracy measure), ridge regression was the most accurate ML technique on 200 simulated datasets (F1 score = 0.475 ± 0.231), a performance which almost doubled when highly-correlated covariates were accounted for (F1 score = 0.860 ± 0.158). These performances were better than forwards selection with SCM (F1 score = 0.251 ± 0.274 and 0.499 ± 0.381 without/with correlations respectively). In terms of computational cost, ridge regression (0.42 ± 0.07 seconds/simulated dataset, 1 thread) was ~20,000 times faster than SCM (2.30 ± 2.29 hours, 15 threads). On the clinical dataset, prescreening with the selected ML algorithm reduced SCM runtime by 42.86% (from 1.75 to 1.00 days) and produced the same final model as SCM only. In conclusion, we have demonstrated that accounting for highly-correlated covariates improves ML prescreening accuracy. The choice of ML method and the proportion of important covariates (unknown a priori) can be guided by simulations.

The aim of this study was to investigate the in-use compatibility of eight commercially available closed system transfer device brands (CSTDs) with a formulated model antibody drug conjugate (ADC). Overall, in-use simulated dosing preparation applying the CSTD systems investigated raised concerns for several product quality attributes. The incompatibilities observed were mainly associated with increased visible and subvisible particles formation as well as significant changes in holdup volumes. Visible and subvisible particles contained heterogeneous mixtures of particle classes, with the majority of subvisible particles associated with silicone oil leaching from CSTD systems during simulated dose preparation upon contact with the ADC formulation. These observations demonstrate that CSTD use may adversely impact product quality and delivered dose which could potentially lead to safety and efficacy concerns during administration. Other product quality attributes measured including turbidity, color, ADC recovery, and purity by size exclusion HPLC, did not show relevant changes. It is therefore strongly recommended to test and screen the compatibility of CSTDs with the respective ADC, in a representative in-use simulated administration setting, during early CMC development, i.e., well before the start of clinical studies, to include information about compatibility and to ensure that the CSTD listed in the manuals of preparation for clinical handling has been thoroughly assessed before human use.