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Pharmaceutical profiling assays generate much useful data that contributes to the lead optimization process in early drug discovery. The solubility, permeability, and metabolic stability of compounds need to be optimized simultaneously with potency, functional activity, cellular activity, selectivity and other biological parameters. However, the data from all of these assays for the numerous compounds prepared by parallel synthesis makes the interpretation of results challenging. The use of multivariate data analysis and design of experiments software helps in the effective utilization of all data in the optimization process.

Toxicology biomarkers are structural or functional measurements that correlate with an adverse change in the physiology of an organism. A familiar example is elevated serological alanine aminotransferase (ALT) activity as a biomarker of hepatic injury. Toxicology biomarkers such as ALT help us select compounds with desirable margins of safety in the lead optimization (LO) phase of drug development, and allow us to identify target-organ toxicities that should be monitored in patients during clinical drug development. Unfortunately, useful clinical and pre-clinical biomarkers are not available for many compound-induced pathologies. Promising new molecular techniques that can address this gap by finding candidate biomarkers include genomics, proteomics, and metabonomics. Successful application of these technologies to develop toxicology biomarkers could increase the quality and number of clinical candidates selected during LO, and improve our ability to monitor drug safety in patients. In this chapter we present a case history using toxicogenomics in which DMBT1 was developed as a transcriptional biomarker of bile duct hyperplasia induced via oval cell proliferation, a toxicologic pathology for which biomarkers are needed.

Idiosyncratic drug reactions (IDR) remain an important cause of late development program failure or drug withdrawal. They represent a significant economic cost and patient burden. Although our understanding of IDR continues to grow, our ability to predict and avoid these reactions remains unacceptably poor. The intent of this chapter is to review approaches that may be used during lead optimization to minimize the likelihood of producing compounds for development that will have a high IDR liability.

As the pace of pharmaceutical drug discovery quickens and greater numbers of preclinical candidates are identified using combinatorial and other high throughput methods, the demand on safety assessment assays increases. As most toxicology assays are, at best, medium throughput, it is readily apparent that rapid assessment protocols must be developed and validated for their use in the early discovery phase. No strangers to the increased demand for accurate safety assessments of candidate compounds and the additional constraints imposed by limited resources, regulatory agencies have long been at the forefront of utilizing and championing computational methods. As regulatory databases of safety information are populated and legacy data incorporated, methods to utilize this data to extract meaningful information must be developed and validated. As this is not intended to be an exhaustive review of all tools for toxicology assessment, the reader is referred to a number of recent articles which do an outstanding job of summarizing the algorithms, benefits and shortcomings of many of the commercial packages available (; ; ).

A quick view of recent periodicals clearly point to the challenges of the pharmaceutical industry as we progress through the first decade of the 21st century — the rising costs of new innovative drugs. Much of the current costs and the continued rising costs of drug prices stem from the high price of drug discovery and development. Many sources have cited studies that estimate the cost of pharmaceutical drug development; all of which conclude the same — pharmaceutical R&D costs increase at a rate much greater than inflation. A study conducted in 1987 () concluded the average cost of the development of a new chemical entity (NCE) to be $108 million. This rate has grown dramatically over the past 15 years to the current estimates of over $800 million (; ). The increase in R&D costs is due mainly to the increased cost of animal testing and conducting clinical trials (). The cost of an individual NCE, which is quickly approaching 1 billion dollars, is not the complete source of the large increase in pharmaceutical R&D over the past decade. The development failures of NCEs contribute substantially to this bottom line. One out of every 10 compounds that enter into human testing successfully completes clinical development and is made available to patients. The costs of these failures must be reconciled by the successful marketing of innovative new drugs. The Food and Drug Administration (FDA) recently published a white paper titled “Innovation or Stagnation? - Challenge and Opportunity on the Critical Path to New Medical Products” ().

Assessing brain exposure continues to be a central theme for multiple therapeutic areas within the pharmaceutical industry. In addition to optimizing delivery to CNS targets, brain exposure is considered for unwanted CNS access for either on-target activity or for off-target CNS toxicity or adverse events. The biopharmaceutical scientist is challenged to arrive at a rational strategy that is functional within the constraints of limited resources. Common strategies are integrated combinations of , , and methods (). The appropriate strategy used depends upon the need, i.e., to drive chemistry or to establish a pharmacokinetic-pharmacodynamic relationship. We believe that a rigorous strategy is best so that the best lead series are selected. The intent is to anticipate liabilities of a lead series such that subsequent lead optimization cycle time and clinical attrition rates are ultimately reduced. The rigorous methods should deliver value by aiding synthetic chemistry direction while filtering out difficult templates. We also advocate the use of animal models as early as possible to establish a realistic perspective around the plethora of higher-throughput screening assay data. This application obviously challenges one to increase the capacity of these assays without compromising data quality or wasting vital and limited people resources.

Defining the sequence of the human genome has brought with it a wealth of potential drug targets of uncertain biology. For pharmaceutical firms to reduce the risk of drug failure after long, expensive clinical development programs, accurate information is required about the biological effects of the compounds being tested. Therefore, there is a strong need for biomarkers that can assist in unraveling the complex biology of these novel drug targets and their use during the drug development process is no longer optional.

Membrane transporters represent an important class of proteins responsible for regulating cellular and physiological solute and fluid balance. With the sequencing of the human genome, it has been estimated that approximately 500 to 1,200 genes code for transport proteins (). In terms of xenobiotic disposition, a smaller fraction of these transporters are currently known to significantly interact with drugs. In particular, transporters that are localized at key gateway tissues within the body such as the intestine (; ), liver (; ), kidney (; ), placenta (), and brain (; ) are critical modulators of drug absorption, tissue distribution and elimination and have been the subject of recent reviews.