Progress in predicting human ADME parameters in silico

Understanding the development of a scientific approach is a valuable exerci se in gauging the potential directions the process could take in the future . The relatively short history of applying computational methods to absorpt ion, distribution, metabolism and excretion (ADME) can be split into define d periods. The first began in the 1960s and continued through the 1970s wit h the work of Corwin Hansch et al. Their models utilized small sets of in v ivo ADME data. The second era from the 1980s through 1990s witnessed the wi despread incorporation of in vitro approaches as surrogates of in vivo ADME studies. These approaches fostered the initiation and increase in interpre table computational ADME models available in the literature. The third era is the present were there are many literature data sets derived from in vit ro data for absorption, drug-drug interactions (DDI), drug transporters and efflux pumps [P-glycoprotein (P-gp), MRP], intrinsic clearance and brain p enetration, which can theoretically be used to predict the situation in viv o in humans. Combinatorial synthesis, high throughput screening acid comput ational approaches have emerged as a result of continual pressure on pharma ceutical companies to accelerate drug discovery while decreasing drug devel opment costs, The goal has become to reduce the drop-out rate of drug candi dates in the latter, most expensive stages of drug development. This is acc omplished by increasing the failure rate of candidate compounds in the prec linical stages and increasing the speed of nomination of likely clinical ca ndidates. The industry now understands the reasons for clinical failure oth er than efficacy are mainly related to pharmacokinetics and toxicity. The l ate 1990s saw significant company investment in ADME and drug safety depart ments to assess properties such as metabolic stability, cytochrome P-450 in hibition, absorption and genotoxicity earlier in the drug discovery paradig m. The next logical step in this process is the evaluation of higher throug hput data to determine if computational (in silico) models can be construct ed and validated from it. Such models would allow an exponential increase i n the number of compounds screened virtually for ADME parameters, A number of researchers have started to utilize in silico, in vitro and in vivo appr oaches in parallel to address intestinal permeability and cytochrome P-450- mediated DDI. This review will assess how computational approaches for ADME parameters have evolved and how they are likely to progress. (C) 2001 Else vier Science Inc. All lights reserved.