1.1 INTRODUCTION
The sequencing of the human genome and the emergence of other omics-based technologies have provided drug discoverers with powerful new tools that can be used as a framework for understanding disease mechanisms and predicting patient outcomes (Venter, 2000; Venter et al., 2001; Castle et al., 2002; Kennedy, 2002; Goodsaid, 2003; Guerreiro et al., 2003; Witzmann and Grant, 2003; Walgren and Thompson, 2004; Robertson, 2005; Kell, 2006; Lindon et al., 2007; Clarke and Haselden, 2008). Since the turn of the century, pharmaceutical scientists have been able to incorporate these approaches into their work: to identify specific molecular targets involved in disease initiation and progression; to establish links between animal models and clinical activity at the level of genes, proteins, and pathways; and to devise new ways of measuring and monitoring drug response. In contrast to finding drugs that act at proven drug targets and behaved “correctly” in established preclinical tests, discovery efforts were directed toward screening against sets of novel and sometimes closely related molecular targets that had not yet been thoroughly validated in medical practice, using new preclinical models and assays to confirm therapeutic benefits and define potential toxicities, and streamlined development strategies to obtain early proof of concept in clinical trials (Food Drug Administration, 2006a; Sarapa, 2007; Butz and Morelli, 2008; Takimoto, 2008). Importantly, the vast multidimensional data sets generated by genomics, proteomics, metabolomics, and other reductionist approaches were accompanied by the development of new computational methods needed to cut through the noise and variability associated with in these complex measurements and to assign therapeutic significance to the data. The emergence of systems biology provided an organizational framework that attempted to address the need to reconstitute these data sets into a functioning organic whole (Butcher et al., 2004; Hood and Perlmutter, 2004; Fischer, 2005; Edwards and Preston, 2008).
Not surprisingly, as more innovation and opportunity entered the drug discovery process, the risk of clinical failure did not always go down, except perhaps in cases where disease or toxicity was found to have a relatively straightforward etiology involving a single gene or a well-characterized and understood biochemical process. Despite impressive technological advances, late-stage attrition remained a problem in drug development, and serious and sometimes rare or unexpected adverse events continued to be seen during clinical investigations or postapproval (Arrowsmith, 2011a, b; Arrowsmith and Miller, 2013). Regulatory agencies interpreted this unexpected attrition to indicate that critical gaps still existed in the preclinical testing pathway and the translation of preclinical toxicology findings to clinical outcomes of interest. Some of these critical gaps can be traced to how regulatory toxicology studies are currently conducted. These studies tend to use healthy animals and are designed to identify robust toxicities that depend on dose and exposure rather than conditional effects triggered by individual susceptibilities or interactions with disease and disease comorbidities. Toxicology studies are also designed to characterize the possibility and type of toxicity and to suggest an initial “safe” human dose range rather than to determine the expected clinical prevalence and magnitude of the effect. In some cases, species differences in basic physiology and how a drug may be transported or biotransformed will confound the translation of preclinical findings to human patients. As a result, while preclinical safety data can reasonably predict clinical risk under appropriate testing conditions (Ewart et al., 2014; Holzgrefe et al., 2014), a lack of concordance can sometimes be found between preclinical and clinical findings, including the observation of toxicities in animal models that have no observed correlate in clinical experience (Olson et al., 1998, 2000; Alden et al., 2011; Wang and Gray, 2014).
To help address these issues and promote the advancement of new technologies, the FDA has issued several documents that define key regulatory science priorities as well as a process for introducing new tools into drug development. Beginning with the publication of the FDA’s Critical Path Initiative and Opportunities List in 2004, these documents highlight the need for new methods in toxicology, including the evaluation and development of more predictive models and assays; the identification and performance characterization of more reliable biomarkers; and the application of in silico approaches and large data sets to organize and interpret diverse safety data (Food Drug Administration, 2004a, b, 2006b, 2011; Woodcock, 2007). In parallel and in response, the pace of scientific innovation has accelerated, with numerous emerging technologies being positioned as transformative new drug development tools with the potential to improve safety assessment and reduce the possibility of late-stage attrition. Recent attempts to “humanize” animal models (Cheung and Gonzalez, 2008; Zhang et al., 2009; Shultz et al., 2012) and to replicate human response in vitro using organotypic cultures (Schmeichel and Bissell, 2003; Huh et al., 2011; Mathur et al., 2013; Sung et al., 2013; Abaci and Shuler, 2015) and induced pluripotent stem cells (iPSCs) (Sirenko et al., 2013, 2014a, b; Kolaja, 2014; Doherty et al., 2015) have opened additional avenues for assessing human drug safety and efficacy. New in silico and in vitro approaches are being proposed to assess the risk of drug-induced proarrhythmia (Mirams et al., 2011, 2012; Johannesen et al., 2014; Sager et al., 2014) and to strengthen safety signals detected during postmarket pharmacovigilance (Szarfman et al., 2004; Harpaz et al., 2013; Liu et al., 2013; White et al., 2013).
In some cases, new regulatory pathways have been developed to improve the prediction of clinical risk based on fresh insights into toxicity mechanisms. One example is using assays based on the human ether-a-go-go-related gene (hERG) channel, which is believed to encode the native cardiac potassium channel responsible for generating the rapid delayed rectifier potassium current (IKr) in the human heart (Kiehn et al., 1995; Sanguinetti et al., 1995). The recognition that some drugs can trigger torsade de pointes (TdP), a serious and usually fatal cardiac arrhythmia, by excessively prolonging ventricular repolarization through block of IKr led to the development of a new approach for assessing cardiac safety, currently embodied in the International Council on Harmonisation (ICH) S7B and E14 guidelines (FDA, 2005a, b; ICH, 2005). This new pathway involves testing drug effects on the hERG channel in a clonal cell line expression system (Hammond and Pollard, 2005), with confirmation of any notable findings in the clinical Thorough QT (TQT) study, which measures changes in the electrocardiographic QT interval (Darpo et al., 2006).
The purpose of this chapter is to identify specific questions that may arise when evaluating the potential regulatory impact of a new technology as well as the type of criteria that can be used to determine whether a new tool has general applicability as a basis for regulatory decision-making in drug development.
