Editorial pubs.acs.org/molecularpharmaceutics
Predictive DMPK: In Silico ADME Predictions in Drug Discovery
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meaning no one scientist thinks holistically about drug properties. Current in silico models are capable of providing valuable information on physicochemical properties, which are known to influence permeability, metabolism, and pharmacokinetics of molecules. The increased sophistication of these tools has already enabled more comprehensive evaluations of druglike chemical space, including identification of leads with optimal properties in multiple categories. This special issue presents innovative approaches to in silico modeling in a wide range of ADMET sciences. Dennis Smith highlights the importance of “lipoidal permeability as being central tenets in drug disposition” and explains the role of transporters, versus metabolic clearance, in elimination. Mark Wenlock and Patrick Barton describe their methodologies for generating physicochemical parameters, such as ionization and aqueous solubility, and Daniel Ortwine describes how these parameters can be used to predict DMPK properties. Kevin Ford discusses the predictive power of electrostatic potential for bioactivation and reactivity toward a nucleic acid as a new approach for predicting toxication and drug-induced mutagenesis. In the field of metabolism, Patrik Rydberg et al. describe the 2-D accessibility descriptors for determining the site of metabolism by cytochrome P450s, and Jeffrey Jones and Ken Korzekwa use an in silico model for determining human aldehyde oxidase substrate recognition. With regard to transporters, Prashant Desai et al. describe a P-glycoprotein efflux model, and Yongmei Pan et al. propose a paradigm for identifying BCRP inhibitors. Andrew Davis and David Wood discuss the performance and value of automated QSAR models, and, finally, Natalie Hosea and Hannah Jones and Ken Grime et al. describe different approaches for predicting pharmacokinetic parameters. The effective use of in silico modeling remains a huge challenge in the pharmaceutical industry. Simulation tools are often used by a subset of scientists who may work in some isolation. Widespread adoption remains an issue; an interesting report details differential uptake among large pharmaceutical companies, suggesting that corporate cultural environment plays a role in model utilization.2 Let us present a challenge to all of us, as an industry, to use these models to explore the ADME chemical space in a proactive manner and not to limit ourselves to using these tools for compounds that have already been synthesized. Completing SAR tables is useful but at times can be limiting. How many times have we looked back at a project and realized that only limited progress was made due to the narrow chemical space explored? The tools described here can help us answer the “what if?” question by broadening our approach and integrating knowledge from a variety of disciplines involved in drug discovery and development. The effective use of in silico modeling will require a shift from the
ver the past 10−15 years, in silico modeling has been increasingly used to predict drug properties facilitating the discovery and development of drug candidates. A range of characteristics, such as physicochemical, metabolic (both rate and sites), permeability, transport, pharmacokinetic, and safety properties, can be predicted through these models. Model performance has improved to the point that, by use of a relatively small data set, the chemical features that drive a certain property can be identified. In our experience, in silico models have led to improved decision-making regarding compound synthesis. In one example described in this issue, Daniel Ortwine’s use of in silico modeling brought about the synthesis of more metabolically stable compounds at Genentech. Thus, in silico models have the potential to enhance and possibly accelerate the lead optimization and candidate selection process. The use of in silico modeling is not unique to the pharmaceutical industry. It is widely and routinely used in other industries ranging from the financial to the aerospace sectors, with varying degrees of success. The predictive power of models is dependent on the assumptions made, the relevance and accuracy of the input data, the adequacy of the model, and the iterative process of refining the model over time. An excellent example of the power of in silico modeling is in aerospace design. Creating a simulation to aid in the design of a space shuttle is rather complex; however, over the years modeling has led to immense savings in time and money and, importantly, enhancement in design. Walter Woltosz has stated that “by developing this complex ... simulation, we assembled many bits of known information and theory to discover something that was unknown prior to having the simulation capability, and that turned out to be extremely valuable”.1 In other words, in silico modeling resulted in the creation of innovative designs that would otherwise have been deemed too risky or might not have been conceived at all. For example, NASA’s space shuttle roll maneuver, which has the shuttle flying upside down to increase payload, was “accidentally” discovered by a simulation, according to Woltosz. Modeling in the aerospace industry may seem to have no connection to in silico modeling in drug discovery. After all, space shuttles fly because we can take advantage of the laws of physics, while the effectiveness of drugs is based on biology, with a higher degree of variability, redundancy, and “unknown elements”. Nonetheless, the pharmaceutical industry would benefit from a look at how simulations are constructed and put to use in aerospace design. If we consider all the parameters and the integrated knowledge required to design a space shuttle, we can start to appreciate the complexity. Woltosz argues that pharmaceuticals are behind the curve for using in silico modeling effectively and mentioned, “there is no greater productivity tool than software!” He mentions that one possible reason could be that, in the aerospace industry, the users of the models are engineers who could be considered generalists, while scientists in the pharmaceutical industry are specialists, © 2013 American Chemical Society
Special Issue: Predictive DMPK: In Silico ADME Predictions in Drug Discovery Published: April 1, 2013 1151
dx.doi.org/10.1021/mp400102t | Mol. Pharmaceutics 2013, 10, 1151−1152
Molecular Pharmaceutics
Editorial
“make and test” paradigm to “quality at the point of design”.3 DMPK scientists can play a key role in this shift by interfacing between multiple disciplines and, thus, positively influencing drug discovery projects with the effective use of in silico models.
Jane R. Kenny,* Guest Editor
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Drug Metabolism and Pharmacokinetics, Genentech, Inc., South San Francisco, California 94080, United States
AUTHOR INFORMATION
Corresponding Author
*Phone: 1 650 467 6027. E-mail:
[email protected]. Notes
Views expressed in this editorial are those of the author and not necessarily the views of the ACS.
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REFERENCES
(1) Woltosz, W. S. If we designed airplanes like we design drugs. J. Comput.-Aided Mol. Des. 2012, 26 (1), 159−163. (2) Leeson, P. D.; St-Gallay, S. A. The influence of the “organizational factor” on compound quality in drug discovery. Nat. Rev. Drug Discovery 2011, 10, 749−765. (3) As described by Andrew Davis and David Wood in this issue.
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