Abstract
Today, a growing number of computational aids and simulations are shaping model-informed drug development. Artificial intelligence, a family of self-learning algorithms, is only the latest emerging trend applied by academic researchers and the pharmaceutical industry. Nanomedicine successfully conquered several niche markets and offers a wide variety of innovative drug delivery strategies. Still, only a small number of patients benefit from these advanced treatments, and the number of data sources is very limited. As a consequence, “big data” approaches are not always feasible and smart combinations of human and artificial intelligence define the research landscape. These methodologies will potentially transform the future of nanomedicine and define new challenges and limitations of machine learning in their development. In our review, we present an overview of modeling and artificial intelligence applications in the development and manufacture of nanomedicines. Also, we elucidate the role of each method as a facilitator of breakthroughs and highlight important limitations.
Highlights
In ancient theater, the Latin calque “deus ex machina”, the “god from the machine”, referred to a crane or trapdoor used to suspend objects on stage
A rising number of computational aids has been applied in drug development
While the technical methods and algorithms are in place, their application in nanomedicine is widely driven by the availability of data
Summary
The Latin calque “deus ex machina”, the “god from the machine”, referred to a crane or trapdoor used to suspend objects on stage. Model-informed drug development (MIDD) refers to the application of a wide variety of quantitative models derived from preclinical and clinical data to facilitate early decision-making in drug development [1] They were formally recognized by the US-FDA in 2017 as part of their performance goals and procedures commitment letter and involve exposure-based, biological, and statistical models which can give support to establish more successful therapeutic regimens of drug products and increase the chances of approval by regulatory agencies [2]. The risks associated with a critical failure are a strong incentive for the application of modern technologies, modeling, and simulation in their design and evaluation With regard to their in vivo behavior, while many other formulation approaches can rely on a rich knowledge base, the small market share together with poor accessibility of data requires smart extrapolations and model designs to make their preclinical and clinical outcomes more predictable [3]. The combination between modeling and AI may solve this problem as prior modeling can help to split the overall problem into several subproblems
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