Application of In Silico, In Vitro and In Vivo ADMET/PK Platforms in Drug Discovery

Chapter

Abstract

Drug discovery is a risky and expensive business fraught with high attrition rates. The health-care benefits and financial rewards that can be realised if successful have, however, ensured the relevance and continued growth of the pharmaceutical industry. The major reasons for high attrition rates during drug discovery include lack of efficacy, toxicity, inadequate pharmacokinetics (PK), and market forces, factors which can be complexly interrelated. PK has been shown to affect the efficacy and safety of new chemical entities (NCEs). The pharmaceutical industry responded by frontloading the characterisation of the processes that determine the PK of compounds, that is, absorption, distribution, metabolism and excretion (ADME). The ADME data are being used to guide medicinal chemists in the design of molecules with favourable disease-specific PK properties. In late stages of drug discovery, the preclinical ADME data are being used to predict human PK and safety of NCEs. As African research scientists initiate drug discovery activities, integration of PK, in the traditionally medicinal chemistry- and disease pharmacology-driven efforts, is important towards ensuring increased chances of discovering good candidate drugs (CD). We have therefore set up in silico, in vitro and in vivo ADMET platforms which various African drug discovery scientists can access. Drugs for non-communicable diseases fail more from lack of efficacy than poor PK and vice versa. Drug discovery efforts in Africa are also mainly based on natural products for which little is known or can be inferred from the PK of conventional synthetic drugs. These factors point to the need of promoting the sciences and technologies of PK research at many African institutions.

Keywords

Drug Discovery Liver Microsome Human Liver Microsome Chemical Entity Reactive Metabolite 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abbreviations

ADMET

Absorption, distribution, metabolism, excretion and toxicity

AiBST

African Institute of Biomedical Science and Technology

AUC

Area under the curve

CL

Clearance

Cmax

Maximum plasma concentration

DME

Drug-metabolising enzymes

DMPK

Drug metabolism and pharmacokinetics

Fa

Fraction absorbed

fu

Fraction unbound

HPGL

Hepatocytes per gram of liver

HTS

High-throughput screening

IVIVE

In Vitro to in vivo extrapolation

Kel

Elimination rate constant

LD

Lead discovery

LO

Lead optimisation

MPPG

Microsomal protein per gram

NCEs

New chemical entities

PD

Pharmacodynamics

PK

Pharmacokinetics

PSA

Polar surface area

QSAR

Quantitative structure activity relationship

SAR

Structure activity relationship

t1/2

Half-life

TDI

Time-dependent inhibition

Tmax

Time to reach maximum plasma concentration

Vd

Volume of distribution

References

  1. 1.
    Prentis RA, Lis Y, Walker SR (1988) Pharmaceutical innovation by the seven UK-owned pharmaceutical companies (1964–1985). Br J Clin Pharmacol 25:387–396CrossRefGoogle Scholar
  2. 2.
    Kola I, Landis J (2004) Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 3:711–716CrossRefGoogle Scholar
  3. 3.
    Kerns EH, Di L (2008) Drug-like properties: concepts, structure design and methods. From ADME to toxicity optimisation. Elsevier, AmsterdamGoogle Scholar
  4. 4.
    Ana Ruiz-Garcia A, Bermejo M, Moss A et al (2008) Pharmacokinetics in drug discovery. J Pharm Sci 97:654–690CrossRefGoogle Scholar
  5. 5.
    Gilbaldi M (1975) Biopharmaceutics and clinical pharmacokinetics. Lea & Febiger, PhiladelphiaGoogle Scholar
  6. 6.
    Masimirembwa CM, Otter C, Berg M et al (1999) Heterologous expression and kinetic characterization of human cytochromes P-450: validation of a pharmaceutical tool for drug metabolism research. Drug Metab Dispos 27:1117–1122Google Scholar
  7. 7.
    Li XQ, Björkman A, Andersson TB et al (2003) Identification of human cytochrome P(450)s that metabolise anti-parasitic drugs and predictions of in vivo drug hepatic clearance from in vitro data. Eur J Clin Pharmacol 59:429–442CrossRefGoogle Scholar
  8. 8.
    Thelingwani RS, Zvada SP, Hugues D et al (2009) In vitro and in silico identification and characterisation of thiabendazole as a mechanism-based inhibitor of CYP1A2 and simulation of possible pharmacokinetic drug-drug interactions. Drug Metab Dispos 37:1286–1294CrossRefGoogle Scholar
  9. 9.
    Johansson T, Jurva U, Grönberg G et al (2009) Novel metabolites of amodiaquine formed by CYP1A1 and CYP1B1: structure elucidation using electrochemistry, mass spectrometry, and NMR. Drug Metab Dispos 37:571–579CrossRefGoogle Scholar
  10. 10.
    Jurva U, Holmén A, Grönberg G et al (2008) Electrochemical generation of electrophilic drug metabolites: characterization of amodiaquine quinoneimine and cysteinyl conjugates by MS, IR, and NMR. Chem Res Toxicol 21:928–935CrossRefGoogle Scholar
  11. 11.
    Masimirembwa CM, Bredberg U, Andersson TB (2003) Metabolic stability for drug discovery and development: pharmacokinetic and biochemical challenges. Clin Pharmacokinet 42:515–528CrossRefGoogle Scholar
  12. 12.
    Bapiro TE, Sayi J, Hasler JA et al (2005) Artemisinin and thiabendazole are potent inhibitors of cytochrome P450 1A2 (CYP1A2) activity in humans. Eur J Clin Pharmacol 61:755–761CrossRefGoogle Scholar
  13. 13.
    Bapiro TE, Egnell AC, Hasler JA et al (2001) Application of higher throughput screening (HTS) inhibition assays to evaluate the interaction of antiparasitic drugs with cytochrome P450s. Drug Metab Dispos 29:30–35Google Scholar
  14. 14.
    Masimirembwa CM, Thompson R, Andersson TB (2001) In vitro high throughput screening of compounds for favorable metabolic properties in drug discovery. Comb Chem High Throughput Screen 4:245–263CrossRefGoogle Scholar
  15. 15.
    Gwaza L, Wolfe AR, Benet LZ et al (2009) In vitro inhibitory effects of Hypoxis obtusa and Dicoma anomala on cyp450 enzymes and pglycoprotein. Afr J Pharm Pharmacol 3:539–546Google Scholar
  16. 16.
    Chibale K, Guantai E, Masimirembwa C (2011) Extracting molecular information from African natural products to facilitate unique African-led drug-discovery efforts. Future Med Chem 3:257–261CrossRefGoogle Scholar
  17. 17.
    Lipinski C, Lombardo F, Dominy BW et al (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3–25CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of DMPK/PD and ToxicologyAfrican Institute of Biomedical Science and TechnologyHarareZimbabwe
  2. 2.Department of Clinical PharmacologyUniversity of Cape TownCape TownSouth Africa
  3. 3.Department of ChemistryUniversity of Cape TownCape TownSouth Africa

Personalised recommendations