Simcyp Explained

Simcyp Limited
Type:Private
Foundation:Sheffield, UK (2001)
Industry:Pharmacokinetic modelling & simulation
Products:Simcyp Population-based ADME Simulator
Simcyp Paediatric
Simcyp Rat
Homepage:https://www.certara.com

Simcyp Limited is a research-based company which provides modelling and simulation software to the pharmaceutical industry for use during drug development. Simcyp is based in Sheffield, UK.

Simcyp’s Simulators allow in silico prediction of drug absorption, distribution, metabolism excretion (ADME) and potential drug-drug interactions.

Research and development

Simcyp’s R&D activities focus on the development of algorithms along with population and drug databases for modelling and simulation (M&S) of the absorption and disposition of drugs in patients and specific subgroups of patients across different age ranges. The Simcyp models use experimental data generated routinely during pre-clinical drug discovery and development from in vitro enzyme and cellular systems, as well as any relevant physico-chemical attributes of the drug and dosage forms.[1] Some details of the scientific background of Simcyp approaches can be found in recent publications.[2] [3] [4] [5]

Background

Simcyp was originally formed as a spin-out company from the University of Sheffield, UK. The company operates the Simcyp Consortium of pharmaceutical and biotechnology companies. The Consortium acts as a steering committee, guiding scientific research and development at Simcyp. There is also close collaboration with regulatory bodies (the U.S. Food and Drug Administration, Swedish Medical Products Agency, NAM, ECVAM) and academic centres of excellence worldwide, within the framework of the Consortium.

Simulator platforms

Simulator platforms include the Simcyp population-based ADME Simulator,[6] Simcyp Paediatric, Simcyp Rat, Simcyp Dog, and Simcyp Mouse.

Simcyp Simulator

The Simcyp Simulator is a population-based ADME simulator[6] is a modelling and simulation platform used by the pharmaceutical industry in drug discovery and development. The Simulator models drug absorption, distribution, metabolism and elimination using routinely generated in vitro data.

Simcyp simulations are performed in virtual populations, including paediatric populations, rather than an average individual. This allows individuals at extreme risk from adverse reactions to be identified before human studies.[7] The functional capability of the Simcyp Simulator is summarized in the following table.

MetabolismIdentification of the extremes and determinants of population variability in in vivo drug metabolism from in vitro data generated using:Human liver microsomes
Human intestinal microsomes
Human kidney microsomes
Human hepatocytes
Recombinant CYP and UGT enzymes
PK profilesSimulation of full drug and metabolite concentration–time profiles. Prediction of volume of distribution based on lipophilicity, ionisation, protein binding and tissue composition by reference to a 14 organ PBPK model
Drug – drug interactionsPrediction of the extent of metabolically-based drug–drug interactions, allowing simultaneous consideration of:Competitive enzyme inhibition Irreversible, mechanism (time)-based enzyme inhibition (including auto-inhibition)
Enzyme-induction (including auto-induction)
Multiple interactions (involving up to four drugs plus two metabolites) with complex study designs
AbsorptionThe Simcyp advanced dissolution absorption and metabolism (ADAM) model incorporates factors influencing the rate and extent of oral drug absorption including:
Gastric emptying rate and intestinal and colon transit times Regio- and age-specific luminal pH as it affects ionisation, solubility, chemical stability, permeability, dissolution and precipitation GI tract surface area and regional variation in permeability and enzyme and transporter densityLuminal fluid volumes and dynamics Fed versus fasting states It allows evaluation of immediate and modified release formulations and the impact of particle size on dissolution rate
Virtual patient populationsSimcyp population databases include North European Caucasians, Japanese, healthy volunteers (for virtual Phase I studies) as well as obese/morbidly obese individuals and patients with renal impairment (moderate or severe), and liver cirrhosis (Child-Pugh A, B or C)
Trial designFlexible trial design options facilitate different routes of drug administration and a variety of dosing options including single/multiple dosing and dose staggering
PaediatricA full PBPK model, supported by extensive libraries on demographics, developmental physiology and the ontogeny of drug elimination pathways allows prediction of PK behaviour in neonates, infants and children

External links

Notes and References

  1. Rostami-Hodjegan A, Tucker GT. Simulation and prediction of in vivo drug metabolism in human populations from in vitro data. Nature Reviews Drug Discovery. 6. 2. 140–8. February 2007. 17268485. 10.1038/nrd2173.
  2. Yang J, Jamei M, Yeo KR, Tucker GT, Rostami-Hodjegan A. Prediction of intestinal first-pass drug metabolism. Curr. Drug Metab.. 8. 7. 676–84. October 2007. 17979655. 10.2174/138920007782109733.
  3. Yang J, Jamei M, Yeo KR, Tucker GT, Rostami-Hodjegan A. Theoretical assessment of a new experimental protocol for determining kinetic values describing mechanism (time)-based enzyme inhibition. Eur J Pharm Sci. 31. 3–4. 232–41. July 2007. 17512176. 10.1016/j.ejps.2007.04.005.
  4. Perrett HP, etal. 2007. Disparity in holoprotein/apoprotein ratios of different standards used for immunoquantification of hepatic cytochrome P450 enzymes.. Drug Metabolism and Disposition. 35. 10. 1733–1736. 10.1124/dmd.107.015743. 17600083.
  5. Yang J, Jamei M, Yeo KR, Rostami-Hodjegan A, Tucker GT. Misuse of the well-stirred model of hepatic drug clearance. Drug Metab. Dispos.. 35. 3. 501–2. March 2007. 17325025. 10.1124/dmd.106.013359.
  6. Jamei M, Marciniak S, Feng K, Barnett A, Tucker G, Rostami-Hodjegan A. The Simcyp((R)) Population-based ADME Simulator. Expert Opin Drug Metab Toxicol. 5. 2. 211–223. February 2009. 19199378. 10.1517/17425250802691074.
  7. Rostami-Hodjegan A, Tucker GT. Simulation and prediction of in vivo drug metabolism in human populations from in vitro data. Nat Rev Drug Discov. 6. 2. 140–8. February 2007. 17268485. 10.1038/nrd2173.