Amin Rostami-Hodjegan
and Geoff Tucker
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 and 2. In this context, it is vital to
appreciate the difference between a useful “simulation” and a precise
“prediction”.
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 IVIVE depends on the use of appropriate models and the
availability of high-quality values for “all” model parameters from the in vitro
data. Accordingly, many attempts at IVIVE, particularly those reported by the
industry [3], lack sufficient key pieces of prior information or ignore obvious
deficiencies in the data, resulting in unjustified claims that the process is
not sufficiently ‘quantitative’ 1 and 2.
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 . 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 ‘talk’
to clinical PK-PD) and failure to acquire all of the necessary information at
the right time in drug development (front-loading). Moreover, some of the tasks
involved in building optimal in silico models require a joint effort on
the part of several organisations, with implications for intellectual property
(IP). However, as emphasised recently in the FDA's whitepaper on the crucial
path to development of new drug products (http://www.fda.gov/oc/initiatives/criticalpath/
accessed in March 2004), without a concerted effort it is probable that many
important opportunities will be missed. The use of in silico simulations
to make better use of in vitro data during the development process could
be one such opportunity
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