Making Transporter Models for Drug–Drug Interaction Prediction Mobile

Abstract

The past decade has seen increased numbers of studies publishing ligand-based computational models for drug transporters. Although they generally use small experimental data sets, these models can provide insights into structure-activity relationships for the transporter. In addition, such models have helped to identify new compounds as substrates or inhibitors of transporters of interest. We recently proposed that many transporters are promiscuous and may require profiling of new chemical entities against multiple substrates for a specific transporter. Furthermore, it should be noted that virtually all of the published ligand-based transporter models are only accessible to those involved in creating them and, consequently, are rarely shared effectively. One way to surmount this is to make models shareable or more accessible. The development of mobile apps that can access such models is highlighted here. These apps can be used to predict ligand interactions with transporters using Bayesian algorithms. We used recently published transporter data sets (MATE1, MATE2K, OCT2, OCTN2, ASBT, and NTCP) to build preliminary models in a commercial tool and in open software that can deliver the model in a mobile app. In addition, several transporter data sets extracted from the ChEMBL database were used to illustrate how such public data and models can be shared. Predicting drug-drug interactions for various transporters using computational models is potentially within reach of anyone with an iPhone or iPad. Such tools could help prioritize which substrates should be used for in vivo drug-drug interaction testing and enable open sharing of models. IntroductionWe are increasingly seeing medium-or high-throughput screens used to develop ligand-based models for individual transporters (Diao et al., 2009Zheng et al., 2009;Kido et al., 2011;Astorga et al., 2012;Ekins et al., 2012b;Greupink et al., 2012;Dong et al., 2013Dong et al., , 2014Sedykh et al., 2013;Wittwer et al., 2013;Xu et al., 2013). One of the significant limitations of this is that the models developed are rarely accessible outside of the research group developing them, likely because of the commercial software required. One way to surmount this is to develop models using open-source software. We previously showed that such "open models" produce validation statistics that are comparable to commercial tools (Gupta et al., 2010). Because many computational machine learning methods use molecular function class fingerprints of maximum diameter 6 (FCFP6) and extended connectivity fingerprints (ECFP6), we have described their implementation with the Chemistry Development Kit (CDK) (Steinbeck et al., 2003) components (Clark et al., 2014). We also recently described how an open-source Bayesian algorithm can be used with these descriptors to develop and validate thousands of data sets, including those from the ChEMBL database . In response to the shift toward mobile computing, we have developed apps for drug discovery, leveraging years of research in cheminformatics...

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