Abstract
Crystalline organic nanoparticles and their amorphous equivalents (ONP) have the potential to become a next-generation formulation technology for dissolution-rate limited biopharmaceutical classification system (BCS) class IIa molecules if the following requisites are met: (i) a quantitative understanding of the bioavailability enhancement benefit versus established formulation technologies and a reliable track record of successful case studies are available; (ii) efficient experimentation workflows with a minimum amount of active ingredient and a high degree of digitalization via, e.g., automation and computer-based experimentation planning are implemented; (iii) the scalability of the nanoparticle-based oral delivery formulation technology from the lab to manufacturing is ensured. Modeling and simulation approaches informed by the pharmaceutical material science paradigm can help to meet these requisites, especially if the entire value chain from formulation to oral delivery is covered. Any comprehensive digitalization of drug formulation requires combining pharmaceutical materials science with the adequate formulation and process technologies on the one hand and quantitative pharmacokinetics and drug administration dynamics in the human body on the other hand. Models for the technical realization of the drug production and the distribution of the pharmaceutical compound in the human body are coupled via the central objective, namely bioavailability. The underlying challenges can only be addressed by hierarchical approaches for property and process design. The tools for multiscale modeling of the here-considered particle processes (e.g., by coupled computational fluid dynamics, population balance models, Noyes–Whitney dissolution kinetics) and physiologically based absorption modeling are available. Significant advances are being made in enhancing the bioavailability of hydrophobic compounds by applying innovative solutions. As examples, the predictive modeling of anti-solvent precipitation is presented, and options for the model development of comminution processes are discussed.
Highlights
Modeling and simulation of the full oral delivery process chain for drug formulations can serve the ultimate task of accurately predicting in vivo pharmacokinetics of a new potential drug [1] by providing a quantitative model for drug manufacturing and delivery.This is of particular interest for the many new molecular entities identified by pharmaceutical industry screening programs exhibiting poor water solubility [2], which makes their formulation difficult or even impossible
These remarkable results shows that (i) the precipitation of organic drug organic nanoparticles and their amorphous equivalents (ONP) in the range of a few 10 nm is possible by proper stabilization, (ii) that the obtained particle size distributions can be predicted by a knowledge-based quantification of mixing and particle formation and that (iii) the obtained size distributions are self-similar, which is the basis for scale-up to large scale [50]
While the tools are available and “just” need to be further improved, their multiscale implementation for the predictive design of unit operations strongly depends on available material parameters such as mechanical and thermodynamic properties in dependence of particle size and shape
Summary
Modeling and simulation of the full oral delivery process chain for drug formulations can serve the ultimate task of accurately predicting in vivo pharmacokinetics of a new potential drug [1] by providing a quantitative model for drug manufacturing and delivery. Sole machine learning or artificial intelligence-based algorithms are used in many applications along the pharmaceutical development pipeline [5], whereas the prediction of physical chemical properties of compounds such as distribution equilibria, solubility, or melting point [6], as well as more complex tasks, for instance absorption, distribution, metabolism, excretion, and toxicity [7] or retrosynthesis [8], the in silico prediction of formulation performance is far less established Such data-driven models can be utilized to support formulation development in a wide range of different quantities, e.g., in vitro performance [9], stability [10], or disintegration time [11].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
