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. 2012 Nov 7;1(11):e13.
doi: 10.1038/psp.2012.14.

A Mechanistic, Model-Based Approach to Safety Assessment in Clinical Development

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Free PMC article

A Mechanistic, Model-Based Approach to Safety Assessment in Clinical Development

J Lippert et al. CPT Pharmacometrics Syst Pharmacol. .
Free PMC article

Erratum in

Abstract

Assessing the safety of pharmacotherapies is a primary goal of clinical trials in drug development. The low frequency of relevant side effects, however, often poses a significant challenge for risk assessment. Methodologies allowing robust extrapolation of safety statistics based on preclinical data and information from clinical trials with limited numbers of patients are hence needed to further improve safety and efficacy in the drug development process. Here, we present a generic systems pharmacology approach integrating prior physiological and pharmacological knowledge, preclinical data, and clinical trial results, which allows predicting adverse event rates related to drug exposure. Possible fields of application involve high-risk populations, novel drug candidates, and different dosing scenarios. As an example, the approach is applied to simvastatin and pravastatin and the prediction of myopathy rates in a population with a genotype leading to a significantly increased myopathy risk.CPT: Pharmacometrics & Systems Pharmacology (2012) 1, e13; doi:10.1038/psp.2012.14; advance online publication 7 November 2012.

Figures

Figure 1
Figure 1
Systems pharmacology. A generic systems pharmacology approach exploits drug safety–related information to predict adverse event rates. Physiology-based pharmacokinetic models are used to represent large amounts of prior physiological, anatomical, and pharmacological information and knowledge. The workflow additionally integrates preclinical data and clinical trial results routinely generated drug development. Together with the existing knowledge of genetic risk factors, the approach quantifies potential safety issues in high-risk patient populations. GI, gastro-intestinal; RD, embryonal rhabdomyosarcoma cells; SNP, single-nucleotide polymorphism.
Figure 2
Figure 2
Workflow for model-based safety assessment. (1) Establishment of reference PBPK models; (2) model evaluation at relevant scales; (3) simulation of virtual populations and model evaluation; (4) calculation of TD markers; (5) evaluation of the safety risk; (6) prediction of the safety risk in high-risk patient groups or the risk for a novel drug candidate as a (a) dose to dose, (b) drug to drug, or (c) patient to patient extrapolation. ADME, absorption, distribution, metabolism, and excretion; PBPK, physiology-based pharmacokinetic; TD, toxicodynamic.
Figure 3
Figure 3
Validation of physiology-based pharmacokinetic (PBPK) models for simvastatin acid and pravastatin at the organism level and at the molecular scale. (a,b) Model-based prediction of pharmacokinetic phenotypes. After adjustment of model parameters with respect to the homozygous genotype TT (black; experiment: triangle (up)), the minor frequent homozygous genotype (CC, light gray; experiment: triangle (down)) was simulated by decreasing the transporter activity for (a) simvastatin acid and (b) pravastatin(all simulations are indicated by a solid line). Taking the average of the transporter activity of both homozygous genotypes correctly predicts the plasma curves of the heterozygous genotype (TC, dark gray; triangle diamonds) for both drugs. (c) Model-based prediction of the effect of the solute carrier organic anion transporter family member 1B1 single-nucleotide polymorphism on the transporter activities at the molecular scale. The ratio of transporter activities formula image is different for simvastatin acid (black) and pravastatin (white). This PBPK model-based finding (left) could be verified with in vitro assays (right) using HEK 293 cells and [³H]-labeled pravastatin and simvastatin acid. *P < 1e-4, randomization test.
Figure 4
Figure 4
Population simulations. Population PK simulations (gray area) describe interindividual variability in clinical data for all three genotypes for (a,b,c) simvastatin acid and (d,e,f) pravastatin (TT, black triangle (up); TC, dark gray diamonds; CC, light gray triangle (down)).,
Figure 5
Figure 5
A toxicodynamic (TD) marker for statin toxicity. (a) The TD marker for pravastatin (dashed line) and simvastatin acid (solid line) is plotted as a function of transporter activity relative to the mean transport activity of the TT genotype. Mean activities for all genotypes are indicated by circles (TT: black, TC: dark gray, CC: light gray) and the extrapolated intermediate values are indicated by a solid line. (b,c) Genotype-specific distributions of the TD marker in population PK studies (TT: black bar, TC: dark gray bar, CC: light gray bar) for 1,000 virtual patients (cumulated distribution, black line) on treatment with (b) simvastatin acid and (c) pravastatin. The distributions are based on the population PK plots in Figure 4.
Figure 6
Figure 6
Prediction of clinical incidence rates. Simulated cumulated distributions of the toxicodynamic (TD) marker are shown as solid lines (TT: black, TC: dark gray, CC: light gray). First-year myopathy incidence rates of TT and TC patients (vertical ranges, right y axis) receiving 80 mg simvastatin in the SEARCH study combined with the simulated cumulative distribution of the TD marker are used to determine a threshold corridor (vertical light gray bar) for the simulated cumulative distribution of the TD marker in different subgroups of patients (dashed horizontal lines). The corresponding intersections of the threshold corridor and the simulated cumulated distributions of the CC genotype are used to forecast the clinical incidence rate found in the SEARCH study (horizontal light gray bar).

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