July 20, 2021
by Gustavo Nativio
Hey there! I‘m the resident intern here at ScitoVation for the summer. As someone deeply interested in systems biology and solving biological problems using mathematics and computational models, the work done by ScitoVation is fascinating to me. ScitoVation is using innovative computational modeling of physiological systems to improve regulation efficiency in the pharmaceutical industry and in the environmental health field!
I work with Physiologically Based Pharmacokinetic (PBPK) modeling. PBPK modeling is a tool to predict the distribution and fate of drugs within an organism. PBPK models include biologically realistic descriptions of tissues and processes involved in the absorption, distribution, metabolism, and excretion (ADME) of a compound. PBPK models provide a mechanistic approach to study and predict the pharmacokinetics of chemicals or drugs based on physiologic and anatomic characteristics, as well as the physical and chemical properties of a given chemical or drug. These models are used in the field of toxicology for the prediction of human and animal exposures to environmental toxins and in the drug industry to help in drug development.
For my internship project, I will work on a new concept that is gaining more and more interest in the industry: the kinetically derived maximum dose (KMD). KMD is a novel method of determining maximum doses of new chemicals or drugs computationally rather than with in vivo methods, which can greatly expedite the regulatory approval process and prevent unnecessary loss of life of experimental animals. This novel method relies on the fact that the relationship between internal and external dose shows an inflection point where linearity goes into non-linearity due to saturation of underlying processes. Non-linear pharmacokinetics can be caused by saturation or limitation of various factors related to absorption, distribution, metabolism, and excretion (ADME). The goal is to show that top doses in toxicity tests should not be above the inflection point, provided human exposures are orders of magnitude lower and toxicity findings at doses above a KMD may not be relevant to human health risks. My first task will be to convert our current PBPK model case study for KMD modeling from its differential equations software (Berkeley Madonna) into a more usable programming language, PLETHEM. PLETHEM stands for Population Life-course Exposure to Health Effects Model, and it is an open-source modeling platform developed by ScitoVation that combines high-throughput exposure prediction programs and PBPK into a single framework. (Have you signed up for the PLETHEM training in October yet?)
Our superb computational toxicology team guides me through learning the complexity of PBPK modeling and how to use it. Learning ScitoVation’s innovation has been invaluable.