The impact of external innovation on new drug approvals: A retrospective analysis.

Pharmaceutical companies are relying more often on external sources of innovation to boost their discovery research productivity. However, more in-depth knowledge about how external innovation may translate to successful product launches is still required in order to better understand how to best leverage the innovation ecosystem. We analyzed the pre-approval publication histories for FDA-approved new molecular entities (NMEs) and new biologic entities (NBEs) launched by 13 top research pharma companies during the last decade (2006-2016). We found that academic institutions contributed the majority of pre-approval publications and that publication subject matter is closely aligned with the strengths of the respective innovator. We found this to also be true for candidate drugs terminated in Phase 3, but the volume of literature on these molecules is substantially less than for approved drugs. This may suggest that approved drugs are often associated with a more robust dataset provided by a large number of institutes. Collectively, the results of our analysis support the hypothesis that a collaborative research innovation environment spanning across academia, industry and government is highly conducive to successful drug approvals.

Discovery of a potent, dual serotonin and norepinephrine reuptake inhibitor.

The objective of the described research effort was to identify a novel serotonin and norepinephrine reuptake inhibitor (SNRI) with improved norepinephrine transporter activity and acceptable metabolic stability and exhibiting minimal drug-drug interaction. We describe herein the discovery of a series of 3-substituted pyrrolidines, exemplified by compound 1. Compound 1 is a selective SNRI in vitro and in vivo, has favorable ADME properties, and retains inhibitory activity in the formalin model of pain behavior. Compound 1 thus represents a potential new probe to explore utility of SNRIs in central nervous system disorders, including chronic pain conditions.

A performance evaluation of three drug-induced liver injury biomarkers in the rat: alpha-glutathione S-transferase, arginase 1, and 4-hydroxyphenyl-pyruvate dioxygenase.

Alanine aminotransferase (ALT) activity is the most frequently relied upon reference standard for monitoring liver injury in humans and nonclinical species. However, limitations of ALT include a lack of specificity for diagnosing liver injury (e.g., present in muscle and the gastrointestinal tract), its inability to monitor certain types of hepatic injury (e.g., biliary injury), and ambiguity with respect to interpretation of modest or transient elevations (< 3× upper limit of normal). As an initial step to both understand and qualify additional biomarkers of hepatotoxicity that may add value to ALT, three novel candidates have been evaluated in 34 acute toxicity rat studies: (1) alpha-glutathione S-transferase (GSTA), (2) arginase 1 (ARG1), and (3) 4-hydroxyphenylpyruvate dioxygenase (HPD). The performance of each biomarker was assessed for its diagnostic ability to accurately detect hepatocellular injury (i.e., microscopic histopathology), singularly or in combination with ALT. All three biomarkers, either alone or in combination with ALT, improved specificity when compared with ALT alone. Hepatocellular necrosis and/or degeneration were detected by all three biomarkers in the majority of animals. ARG1 and HPD were also sensitive in detecting single-cell necrosis in the absence of more extensive hepatocellular necrosis/degeneration. ARG1 showed the best sensitivity for detecting biliary injury with or without ALT. All the biomarkers were able to detect biliary injury with single-cell necrosis. Taken together, these novel liver toxicity biomarkers, GSTA, ARG1, and HPD, add value (both enhanced specificity and sensitivity) to the measurement of ALT alone for monitoring drug-induced liver injury in rat.

The impact of assay technology as applied to safety assessment in reducing compound attrition in drug discovery.

INTRODUCTION: Attrition in the drug industry due to safety findings remains high and requires a shift in the current safety testing paradigm. Many companies are now positioning safety assessment at each stage of the drug development process, including discovery, where an early perspective on potential safety issues is sought, often at chemical scaffold level, using a variety of emerging technologies. Given the lengthy development time frames of drugs in the pharmaceutical industry, the authors believe that the impact of new technologies on attrition is best measured as a function of the quality and timeliness of candidate compounds entering development. AREAS COVERED: The authors provide an overview of in silico and in vitro models, as well as more complex approaches such as 'omics,' and where they are best positioned within the drug discovery process. EXPERT OPINION: It is important to take away that not all technologies should be applied to all projects. Technologies vary widely in their validation state, throughput and cost. A thoughtful combination of validated and emerging technologies is crucial in identifying the most promising candidates to move to proof-of-concept testing in humans. In spite of the challenges inherent in applying new technologies to drug discovery, the successes and recognition that we cannot continue to rely on safety assessment practices used for decades have led to rather dramatic strategy shifts and fostered partnerships across government agencies and industry. We are optimistic that these efforts will ultimately benefit patients by delivering effective and safe medications in a timely fashion.

Predictive Power Estimation Algorithm (PPEA)--a new algorithm to reduce overfitting for genomic biomarker discovery.

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses.

A comparative integrated transcript analysis and functional characterization of differential mechanisms for induction of liver hypertrophy in the rat.

The main goal of the present work was to better understand the molecular mechanisms underlying liver hypertrophy (LH), a recurrent finding observed following acute or repeated drug administration to animals, using transcriptomic technologies together with the results from conventional toxicology methods. Administration of 5 terminated proprietary drug candidates from participating companies involved in the EU Innomed PredTox Project or the reference hepatotoxicant troglitazone to rats for up to a 14-day duration induced LH as the main liver phenotypic toxicity outcome. The integrated analysis of transcriptomic liver expression data across studies turned out to be the most informative approach for the generation of mechanistic models of LH. In response to a xenobiotic stimulus, a marked increase in the expression of xenobiotic metabolizing enzymes (XME) was observed in a subset of 4 studies. Accumulation of these newly-synthesized proteins within the smooth endoplasmic reticulum (SER) would suggest proliferation of this organelle, which most likely is the main molecular process underlying the LH observed in XME studies. In another subset of 2 studies (including troglitazone), a marked up-regulation of genes involved in peroxisomal fatty acid ß-oxidation was noted, associated with induction of genes involved in peroxisome proliferation. Therefore, an increase in peroxisome abundance would be the main mechanism underlying LH noted in this second study subset. Together, the use of transcript profiling provides a means to generate putative mechanistic models underlying the pathogenesis of liver hypertrophy, to distinguish between subtle variations in subcellular organelle proliferation and creates opportunities for improved mechanism-based risk assessment.

Strategic applications of toxicogenomics in early drug discovery.

Productivity issues facing the pharmaceutical industry are numerous, and the current challenges come in the face of an aging population and a demand for new and better medications. These challenges call for improvement in the drug discovery and development process, which paradoxically comes on the heels of remarkable scientific advances and in an era of great opportunity in medical science. Despite these advances, the pharmaceutical industry has yet to translate breakthroughs in new technologies, including genomics, into new drug therapies for unmet medical needs. The strategic application of toxicogenomics to the earliest stages of a drug discovery program offers a valuable opportunity to identify potential safety hurdles earlier than is the norm today. We propose that using genomics predictively (in vitro to predict outcomes in vivo and short-term studies in vivo to predict safety issues in longer studies) can assist in reducing inefficiency in the current paradigm, which is still heavily weighted on traditional endpoints from lengthy in vivo studies. Implementation of these strategies will assist in solving the current pharmaceutical pipeline productivity dilemma of long cycle times and unacceptable attrition rates in the preclinical drug discovery process.

Integration of genomic and metabonomic data in systems biology--are we 'there' yet?

The measurement of genes, proteins and metabolites has gained increasing acceptance as a means by which to study the response of an organism to stimuli, whether they are environmental, genetic, pharmacological, toxicological, etc. Typically referred to as genomics, proteomics, and metabonomics or metabolomics, respectively, these methods as independent entities have undoubtedly provided new biological insight that was not attainable a decade ago. Not surprisingly, scientists continue to push the boundaries to extract knowledge from data, and it is currently recognized that the full realization of these technologies is limited by a lack of tools to enable data integration. Integration of these 'omic datasets, or integromics, is desirable as it links the individual biological elements together to provide a more complete understanding of dynamic biological processes. Accordingly, in addition to developing new data analysis methods to extract further details from each of the high-content datasets individually, effort is also being expended to create or improve statistical methods, databases, annotations and pathway mapping to maximize our learning. There are several recent examples, in both mammalian and non-mammalian systems, in which genes, proteins and/or metabolites have been integrated using either biology- or data-driven strategies. Herein, key findings are reviewed, gaps in our current tools and technologies are identified and illustrated, and perspective is provided on the potential of integromics in biological research.

A clinical flow cytometric biomarker strategy: validation of peripheral leukocyte phospholipidosis using Nile red.

Phospholipidosis, or intracellular accumulation of phospholipids, is caused by specific classes of xenobiotics. This phenomenon represents a challenge for risk assessment that could benefit from the use of biomarkers in the clinical development of new drug candidates. Flow cytometry, coupled with the lipophilic fluoroprobe Nile red, was correlated to histopathology, electron microscopy and inorganic phosphorus detection to validate the utility of this method for monitoring phospholipidosis in peripheral blood leukocytes. Replicate studies with model test compounds were conducted in which F344 rats were given 4 or 7 doses of either maprotiline hydrochloride, imipramine hydrochloride, tilorone dihydrochloride, amikacin hydrate or vehicle control. Transmission electron and light microscopy were used to examine peripheral blood smears and tissue samples for the presence of cytoplasmic vacuoles. Unstained and Nile red stained lysed peripheral blood samples were acquired for analysis using a FACScan flow cytometer. Inorganic phosphorus concentration in the liver was determined from extracted phospholipids and compared with flow cytometry and histological data. It was demonstrated that flow cytometric analysis of Nile red stained lysed whole blood can be used to detect drug-induced phospholipid accumulation in circulating peripheral leukocytes. Furthermore, clinically detectable leukocyte phospholipidosis may be a useful surrogate for coincident or premonitory detection of target organ phospholipidosis.

Pooling samples within microarray studies: a comparative analysis of rat liver transcription response to prototypical toxicants.

Combining or pooling individual samples when carrying out transcript profiling using microarrays is a fairly common means to reduce both the cost and complexity of data analysis. However, pooling does not allow for statistical comparison of changes between samples and can result in a loss of information. Because a rigorous comparison of the identified expression changes from the two approaches has not been reported, we compared the results for hepatic transcript profiles from pooled vs. individual samples. Hepatic transcript profiles from a single-dose time-course rat study in response to the prototypical toxicants clofibrate, diethylhexylphthalate, and valproic acid were evaluated. Approximately 50% more transcript expression changes were observed in the individual (statistical) analysis compared with the pooled analysis. While the majority of these changes were less than twofold in magnitude ( approximately 80%), a substantial number were greater than twofold (approximately 20%). Transcript changes unique to the individual analysis were confirmed by quantitative RT-PCR, while all the changes unique to the pooled analysis did not confirm. The individual analysis identified more hits per biological pathway than the pooled approach. Many of the transcripts identified by the individual analysis were novel findings and may contribute to a better understanding of molecular mechanisms of these compounds. Furthermore, having individual animal data provided the opportunity to correlate changes in transcript expression to phenotypes (i.e., histology) observed in toxicology studies. The two approaches were similar when clustering methods were used despite the large difference in the absolute number of transcripts changed. In summary, pooling reduced resource requirements substantially, but the individual approach enabled statistical analysis that identified more gene expression changes to evaluate mechanisms of toxicity. An individual animal approach becomes more valuable when the overall expression response is subtle and/or when associating expression data to variable phenotypic responses.