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Despite ongoing concerns, the primary metric utilized in bioequivalence studies to quantify absorption rate remains the maximum plasma concentration (Cmax). To more accurately depict absorption rate, the concept of "average slope" (AS) has been recently introduced. The objective of this study is to elucidate and compare the characteristics of AS and Cmax in their representation of the drug-absorption rate. For this purpose, an investigation was conducted on five drugs (nintedanib, methylphenidate, nitrofurantoin, lisdexamfetamine, and theophylline) with different absorption and disposition kinetics. The properties of AS and Cmax, as well as their correlations with other pharmacokinetic parameters, were assessed using supervised and unsupervised machine-learning algorithms, namely principal component analysis, random forest, hierarchical cluster analysis, and artificial neural networks. This study showed that, regardless of the absorption kinetics and across every ML algorithm, AS was more sensitive in reflecting the absorption rate compared to Cmax. In all drugs and methods of analysis, AS demonstrated significantly superior performance in expressing the absorption rate compared to Cmax. The joint use of different techniques complemented each other and verified the findings. Moreover, AS can be easily calculated and has the appropriate units and properties to be used as a metric to express the absorption rate in bioequivalence studies. The adoption of AS by regulatory authorities, as an absorption-rate metric, could significantly improve the accuracy and reliability of BE assessments. Overall, this study focused on addressing the longstanding problem of finding an appropriate absorption-rate metric by demonstrating the desirable properties of AS. [ABSTRACT FROM AUTHOR]

Featured Application: Featured Application: Bioequivalence studies of highly variable drugs require the utilization of large numbers of volunteers. The EMA and FDA propose the utilization of scaled limits. In this study, we introduce the use of artificial neural networks, along with the typical 80–125% limits, as a tool for virtually increasing sample size and thus reducing the actual human exposure. The bioequivalence (BE) of highly variable drugs is a complex issue in the pharmaceutical industry. The impact of this variability can significantly affect the required sample size and statistical power. In order to address this issue, the EMA and FDA propose the utilization of scaled limits. This study suggests the use of generative artificial intelligence (AI) algorithms, particularly variational autoencoders (VAEs), to virtually increase sample size and therefore reduce the need for actual human subjects in the BE studies of highly variable drugs. The primary aim of this study was to show the capability of using VAEs with constant acceptance limits (80–125%) and small sample sizes to achieve high statistical power. Monte Carlo simulations, incorporating two levels of stochasticity (between-subject and within-subject), were used to synthesize the virtual population. Various scenarios focusing on high variabilities were simulated. The performance of the VAE-generated datasets was compared to the official approaches imposed by the FDA and EMA, using either the constant 80–125% limits or scaled BE limits. To demonstrate the ability of AI generative algorithms to create virtual populations, no scaling was applied to the VAE-generated datasets, only to the actual data of the comparators. Across all scenarios, the VAE-generated datasets demonstrated superior performance compared to scaled or unscaled BE approaches, even with less than half of the typically required sample size. Overall, this study proposes the use of VAEs as a method to reduce the necessity of recruiting large numbers of subjects in BE studies. [ABSTRACT FROM AUTHOR]

Background: Allergic contact dermatitis (ACD) is a delayed hypersensitivity reaction occurring in sensitized individuals due to exposure to allergens. Polysensitization, defined as positive reactions to multiple unrelated haptens, increases the risk of ACD development and affects patients' quality of life. The aim of this study is to apply machine learning in order to analyze the association between ACD, polysensitization, individual susceptibility, and patients' characteristics. Methods: Patch test results and demographics from 400 ACD patients (Study protocol Nr. 3765/2022), categorized as polysensitized or monosensitized, were analyzed. Classic statistical analysis and multiple correspondence analysis (MCA) were utilized to explore relationships among variables. Results: The findings revealed significant associations between patient characteristics and ACD patterns, with hand dermatitis showing the strongest correlation. MCA provided insights into the complex interplay of demographic and clinical factors influencing ACD prevalence. Conclusion: Overall, this study highlights the potential of machine learning in unveiling hidden patterns within dermatological data, paving the way for future advancements in the field. [ABSTRACT FROM AUTHOR]

In this study, the modern tool of machine learning is used to address an old problem from a new perspective. Traditionally, the scientific basis for determining bioequivalence is based on a pharmacokinetic comparison, specifically the rate and extent of absorption between two products. Even though it is generally agreed that the peak plasma concentration (Cmax) should be used to measure the rate of absorption, several studies have raised concerns. Thus, alternative pharmacokinetic metrics have been proposed to address Cmax shortcomings. The aim of this study is to utilize unsupervised (principal component analysis) and supervised (random forest) machine learning algorithms to uncover the relationships among the pharmacokinetic parameters and identify the most suitable metric for absorption rate. One actual and three simulated donepezil bioequivalence datasets were utilized. For the needs of this study, a population pharmacokinetic model of donepezil was also developed and further used for the simulation of BE datasets with different absorption kinetics. Among the pharmacokinetic metrics explored, the newly proposed Cmax/Tmax ratio is also investigated. The latter was found to better reflect the absorption rate, regardless of the kinetic properties of absorption. This is one of the first studies utilizing machine learning in the field of bioequivalence. [ABSTRACT FROM AUTHOR]