After more than 40 years of biopolymer development, the current research is still based on conventional laboratory techniques, which require a large number of experiments. Therefore, finding new research methods are required to accelerate and power the future of biopolymeric development. In this study, promising biopolymer–additive ranking was described using an integrated computer-aided molecular design platform. In this perspective, a set of 21 different additives with plant canola and soy proteins were initially examined by predicting the molecular interactions scores and mode of molecule interactions within the binding site using AutoDock Vina, Molecular Operating Environment (MOE), and Molecular Mechanics/Generalized Born Surface Area (MM-GBSA). The findings of the investigated additives highlighted differences in their binding energy, binding sites, pockets, types, and distance of bonds formed that play crucial roles in protein–additive interactions. Therefore, the molecular docking approach can be used to rank the optimal additive among a set of candidates by predicting their binding affinities. Furthermore, specific molecular-level insights behind protein–additives interactions were provided to explain the ranking results. The highlighted results can provide a set of guidelines for the design of high-performance polymeric materials at the molecular level. As a result, we suggest that the implementation of molecular modeling can serve as a fast and straightforward tool in protein-based bioplastics design, where the correct ranking of additives among sets of candidates is often emphasized. Moreover, these approaches may open new ways for the discovery of new additives and serve as a starting point for more in-depth investigations into this area.

Background/Objectives: Despite the recognized importance and distinctive characteristics of the intestinal lymphatic pathway in drug absorption, its pharmacokinetic modeling remains largely unexplored. This study aimed to address this gap by developing a physiologically based pharmacokinetic model (PBPK) to represent the oral lymphatic uptake of halofantrine following a fatty meal. Methods: Using GastroPlus™ 9.8.3 and published literature data, halofantrine absorption, distribution, metabolism, and elimination in both fasting and fed states were modeled. As the used software does not directly simulate intestinal lymphatic transport, lymphatic involvement in the fed state was examined indirectly through parameter adjustments such as first-pass metabolism, pKa-driven solubility changes, and bile-salt-mediated solubilization, with the aid of molecular dynamics simulations under post-prandial pH. Results: The pharmacokinetic models revealed a reduction in the first-pass effect of halofantrine in the fed state compared to that in the fasting state. While adjustments in metabolism kinetics sufficed for constructing a representative PBPK model in the fasting state, capturing the fed-state profile required both modifications to metabolism kinetics and other parameters related to the structural rearrangements of halofantrine driven by the changes in intestinal pH following food intake. These changes were confirmed using molecular dynamics simulations of halofantrine in pHs reflecting the post-prandial conditions. Conclusions: This study underscores the need for further exploration and direct modeling of intestinal lymphatic uptake via PBPK models, highlighting its underexplored status in simulation algorithms. Moreover, the importance of integrating representative physicochemical factors for drugs, particularly in post-prandial conditions or lipid formulations, is evident. Overall, these findings contribute to advancing predictive regulatory and developmental considerations in drug development using post hoc analyses.