Abstract
Regulatory agencies encourage computer modeling and simulation to reduce the time and cost of clinical trials. Although still not classified in formal guidelines, system biology-based models represent a powerful tool for generating hypotheses with great molecular detail. Herein, we have applied a mechanistic head-to-head in silico clinical trial (ISCT) between two treatments for attention-deficit/hyperactivity disorder, to wit lisdexamfetamine (LDX) and methylphenidate (MPH). The ISCT was generated through three phases comprising (i) the molecular characterization of drugs and pathologies, (ii) the generation of adult and children virtual populations (vPOPs) totaling 2,600 individuals and the creation of physiologically based pharmacokinetic (PBPK) and quantitative systems pharmacology (QSP) models, and (iii) data analysis with artificial intelligence methods. The characteristics of our vPOPs were in close agreement with real reference populations extracted from clinical trials, as did our PBPK models with in vivo parameters. The mechanisms of action of LDX and MPH were obtained from QSP models combining PBPK modeling of dosing schemes and systems biology-based modeling technology, i.e., therapeutic performance mapping system. The step-by-step process described here to undertake a head-to-head ISCT would allow obtaining mechanistic conclusions that could be extrapolated or used for predictions to a certain extent at the clinical level. Altogether, these computational techniques are proven an excellent tool for hypothesis-generation and would help reach a personalized medicine.
Highlights
To reduce clinical trials time and cost and to improve their outcomes’ conclusiveness, regulatory agencies encourage the use of computer modeling and simulation (CM&S) approaches to optimize randomized clinical trials [1]
We present here the methods of the Therapeutic Performance Mapping System (TPMS) technology, which allow the generation of virtual patients and physiologically based pharmacokinetic (PBPK) and systems biology-based models with the purpose of performing in silico clinical trial (ISCT)
Sample Size Calculation Since data on treated and non-treated patients is not available, we considered that a number of patients large enough to discriminate among attention-deficit/hyperactivity disorder (ADHD) patients and healthy individuals would be large enough to detect efficacy-associated changes for each drug
Summary
To reduce clinical trials time and cost and to improve their outcomes’ conclusiveness, regulatory agencies encourage the use of computer modeling and simulation (CM&S) approaches to optimize randomized clinical trials [1]. CM&S approaches are based on the analysis of existing data and experience, including real-world data studies, pharmacometrics modeling or, more recently, in silico clinical trials (ISCT). In addition to its economic advantages, ISCT allow the exploration of drugs and diseases in many settings, reducing risks for patients and the use of animal models to test hypotheses. CM&S and artificial intelligence-based approaches are crucial to achieving personalized, preventive, predictive, participative, and precise—the so-called 5P—medicine and healthcare [6].
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