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.
| Company type | Private |
|---|---|
| Industry | Pharmacokinetic modelling & simulation |
| Founded | Sheffield, UK (2001) |
| Headquarters | , United Kingdom  |
| Products | Simcyp Population-based ADME Simulator Simcyp Paediatric Simcyp Rat |
| Website | https://www.certara.com |
Simcyp’s Simulators allow in silico prediction of drug absorption, distribution, metabolism excretion (ADME) and potential drug-drug interactions.
Research and development
editSimcyp’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
editSimcyp 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
editSimulator platforms include the Simcyp population-based ADME Simulator,[6] Simcyp Paediatric, Simcyp Rat, Simcyp Dog, and Simcyp Mouse.
Simcyp Simulator
editThe 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.
| Metabolism | Identification of the extremes and determinants of population variability in in vivo drug metabolism from in vitro data generated using:
Human liver microsomes |
| PK profiles | Simulation 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 interactions | Prediction 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) |
| Absorption | The 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 density Luminal 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 populations | Simcyp 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 design | Flexible trial design options facilitate different routes of drug administration and a variety of dosing options including single/multiple dosing and dose staggering |
| Paediatric | A 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 |
References
edit- ^ Rostami-Hodjegan A, Tucker GT (February 2007). "Simulation and prediction of in vivo drug metabolism in human populations from in vitro data". Nature Reviews Drug Discovery. 6 (2): 140–8. doi:10.1038/nrd2173. PMID 17268485.
- ^ Yang J, Jamei M, Yeo KR, Tucker GT, Rostami-Hodjegan A (October 2007). "Prediction of intestinal first-pass drug metabolism". Curr. Drug Metab. 8 (7): 676–84. doi:10.2174/138920007782109733. PMID 17979655. Archived from the original on July 23, 2012.
{{cite journal}}: CS1 maint: unfit URL (link) - ^ Yang J, Jamei M, Yeo KR, Tucker GT, Rostami-Hodjegan A (July 2007). "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. doi:10.1016/j.ejps.2007.04.005. PMID 17512176.
- ^ Perrett HP, et al. (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. doi:10.1124/dmd.107.015743. PMID 17600083.
- ^ Yang J, Jamei M, Yeo KR, Rostami-Hodjegan A, Tucker GT (March 2007). "Misuse of the well-stirred model of hepatic drug clearance". Drug Metab. Dispos. 35 (3): 501–2. doi:10.1124/dmd.106.013359. PMID 17325025.
- ^ a b Jamei M, Marciniak S, Feng K, Barnett A, Tucker G, Rostami-Hodjegan A (February 2009). "The Simcyp((R)) Population-based ADME Simulator". Expert Opin Drug Metab Toxicol. 5 (2): 211–223. doi:10.1517/17425250802691074. PMID 19199378.
- ^ Rostami-Hodjegan A, Tucker GT (February 2007). "Simulation and prediction of in vivo drug metabolism in human populations from in vitro data". Nat Rev Drug Discov. 6 (2): 140–8. doi:10.1038/nrd2173. PMID 17268485.