Methods to Develop an in silico Clinical Trial: Computational Head-to-Head Comparison of Lisdexamfetamine and Methylphenidate

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.

Keywords: attention-deficit/hyperactivity disorder; in silico clinical trial; lisdexamfetamine; mathematical modeling; methylphenidate.

Figure 1

In silico clinical trial protocol overview. The protocol is divided into three main stages: Phase I, including trial design and information compilation; Phase II, comprising mathematical modeling; and Phase III, consisting of data analysis according to the trial design. ISCT, in silico clinical trial; PBPK, Physiologically based pharmacokinetic; QSP, Quantitative systems biology.

Figure 2

Expected percentage of best accuracy as a function of sample size. Dotted blue and discontinuous green lines correspond to the mean % best accuracy reached for each sample size at statistical power 95 and 99%, respectively, assuming a normal distribution of the accuracy variation and estimating the means and the standard deviation for each sample size. The red line shows the 85% Max accuracy level.

Figure 3

Comorbidities distribution and treatment allocation in the adult virtual population. ADHD, Attention-deficit/hyperactivity disorder; LDX, Lisdexamfetamine; MPH, Methylphenidate; QSP, Quantitative systems biology; vPOP, Virtual population.

Figure 4

Comorbidities distribution and treatment allocation in the pediatric-adolescent virtual population. ADHD, Attention-deficit/hyperactivity disorder; LDX, Lisdexamfetamine; MPH, Methylphenidate; QSP, Quantitative systems biology; vPOP, Virtual population.

Figure 5

Schematic representation of the multi-compartment model for physiologically based pharmacokinetic modeling.

Figure 6

Demographic characteristics (sex, age, BMI, height, and weight) of (A) the adult virtual population (N = 500) and (B) the pediatric-adolescent virtual population (N = 500).

Figure 7

Blood d-Amph and MPH concentration comparison between real datapoints and the curve resulting from the PBPK model. (A) Generated for a standard adult patient after a single 70 mg dose of LDX, real datapoints obtained from Krishnan et al. (66); (B) generated for a standard adult patient after two 10 mg doses of MPH every 4 h, real datapoints obtained from BfArM (68); (C) generated for a standard pediatric patient after a single 50 mg dose of LDX, real datapoints obtained from Boellner et al. (67); and (D) model generated for a standard pediatric patient after three 5 mg doses of MPH every 4 h, real datapoints obtained from Maldonado et al. (69). d-Amph, d-Amphetamine; LDX, Lisdexamfetamine; MPH, Methylphenidate; PBPK, Physiologically based pharmacokinetic.

Figure 8

PCA representation, based on the modulation of ADHD effectors, of LDX and MPH mechanisms of action in (A) ADHD adult patients and (B) ADHD children-adolescent patients. ADHD, Attention-deficit/hyperactivity disorder; LDX, Lisdexamfetamine; MPH, Methylphenidate; PCA, Principal component analysis.