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‘In silico’ simulations to assess the ‘in vivo’ consequences of ‘in vitro’ metabolic drug–drug interactions

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Recently, metabolic drug–drug interactions (M-DDI) have raised some high-profile problems in drug development resulting in restricted use, withdrawal or non-approval by regulatory agencies. The use of in vitro technologies to evaluate the potential for M-DDI has become routine in the drug development process. Nevertheless, in the absence of an integrated approach, their interpretation and value remains the subject of debate, and the vital distinction between a useful “simulation” and a precise “prediction” is not often appreciated. Various in silico softwares are now available for the simulation of M-DDI. However, a concerted effort by the industry is necessary to evaluate their use. The FDA has recently emphasised the importance of such collaboration to improve the crucial path to development of new drugs. In silico simulation of M-DDI has the potential to add significant value to this process.

Section Editors:

Han van de Waterbeemd, Christopher Kohl – Pfizer Global Research & Development, Sandwich Laboratories, PDM (Pharmacokinetics, Dynamics and Metabolism), ipc 664, Ramsgate Road, Sandwich, Kent, UK CT13 9NJ

The propensity of a drug to undergo clinically relevant interactions with concomitant medications can decide on commercial success or failure and in the extreme case even lead to withdrawal of the product from the market. Accurate early prediction of metabolic drug–drug interactions (M-DDI) is therefore a cornerstone of successful drug discovery and development. Amin Rostami-Hodjegan and Geoff Tucker have a long-standing track record in exploring the scientific background of metabolic drug–drug interactions. Their efforts have culminated in the development of the M-DDI prediction software SIMCYP. Here, they review the underlying science of the prediction tools currently available.

Introduction

There have been several high-profile issues in drug development recently relating to problems with metabolic drug–drug interactions (M-DDI) (e.g. with terfenadine, fenfluramine, mibefradil, bromfenac, astemizole and cisapride). The consequences have ranged from restricted use or withdrawal to non-approval by regulatory agencies [see the US Food and Drug Administration (FDA) site (http://www.fda.gov/medwatch/safety.htm) for an updated list].

Recently, there has also been an increased interest in programs and databases that may help to assess the likelihood of M-DDIs by identifying sources of relevant in vitro data and by facilitating access to information on reported interactions. The use of such information, together with the application of predictive models, may expedite the clinical prevention of M-DDIs as well as new drug development.

Although the use of in vitro methods to evaluate the potential for M-DDI has become routine in the drug development process, their interpretation and value remain the subject of debate within the pharmaceutical industry. Part of this controversy relates to the level of confidence in extrapolating from in vitro data to in vivo outcome (IVIVE) 1, 2. In this context, it is vital to appreciate the difference between a useful “simulation” and a precise “prediction”.

Section snippets

Simulation versus prediction

Simulation is but a first step on the road to prediction. In the absence of complete information, in silico IVIVE represents a simulation. Nevertheless, it is valuable in summarising the probable impact of all previous information, in posing “what if” questions, in weighing the importance of missing data and in designing the next real experiment. Once further information becomes available, the simulation moves to becoming a prediction (Fig. 1). The ability to predict M-DDI accurately using

Methods and assumptions

Most approaches to calculate the level of a M-DDI rely on the following equation, describing the average increase in the area under the plasma concentration–time curve (auc; see Glossary for definition of abbreviations) of a ‘victim’ drug following administration of a ‘perpetrator’ drug (after Rowland and Matin [4]):AUC(inhibited)AUC(uninhibited)=1j=1nfmj/foldreductioninCLuint,j+1j=1nfmjwhere fmj is the fraction of substrate clearance mediated by the inhibited metabolic pathway “j” and

Conclusions

Many large pharmaceutical companies are embracing the philosophy of using modelling and simulation technologies, and it has been suggested that in silico approaches may represent up to 15% of R&D spend in the next 5–10 years [22]. However, there are indications that implementation is not always optimal (see Outstanding issues for a list of some outstanding issues). The reasons for this are many, and include excessive ‘compartmentalisation’ of departments (pre-clinical ADME does not always

Outstanding issues

  • Better communication between large databases and predictive programs.

  • Improved collaboration between companies in sharing databases on IVIVE cases.

  • Rationalisation of IP issues related to anonymity and access to data and databases without compromising confidentiality.

  • Commitment to dedicate personnel (realignment) to carry out prospective and retrospective evaluation of IVIVE.

  • Redistribution of expenditure such that frontloading with high quality in vitro data becomes more common and the value of

Related articles

  • Chien, J.Y. et al. (2003) Physiological approaches to the prediction of drug–drug interactions in study populations. Curr. Drug Metab. 4, 347–356

  • Ito, K. et al. (1998) Prediction of pharmacokinetic alterations caused by drug–drug interactions: metabolic interaction in the liver. Pharmacol. Rev. 50, 387–411

  • Tucker, G.T. (1992) The rational selection of drug-interaction studies – implications of recent advances in drug-metabolism. Int. J. Clin. Pharmacol. Therap. 30, 550–553

  • Venkatakrishnan, K. et al

Glossary

AUC
area under concentration–time curve (amount × time/volume).
CLint
intrinsic clearance.
CLR
renal clearance.
CYP
cytochrome P450, a group of enzymes responsible for the metabolism of many xenobiotics.
EH,m
fraction of dug passing through liver metabolised to metabolite of interest (m).
fe
fraction of drug clearance by renal excretion.
fm
fraction of drug clearance by metabolic route of interest (m).
fumic
fraction of drug unbound in microsomal preparations.
FH
fraction of dug passing through liver not

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