Late-stage Attrition Research Articles

Pharmacology-based toxicity assessment: towards quantitative risk prediction in humans.

Despite ongoing efforts to better understand the mechanisms underlying safety and toxicity, ~30% of the attrition in drug discovery and development is still due to safety concerns. Changes in current practice regarding the assessment of safety and toxicity are required to reduce late stage attrition and enable effective development of novel medicines. This review focuses on the implications of empirical evidence generation for the evaluation of safety and toxicity during drug development. A shift in paradigm is needed to (i) ensure that pharmacological concepts are incorporated into the evaluation of safety and toxicity; (ii) facilitate the integration of historical evidence and thereby the translation of findings across species as well as between in vitro and in vivo experiments and (iii) promote the use of experimental protocols tailored to address specific safety and toxicity questions. Based on historical examples, we highlight the challenges for the early characterisation of the safety profile of a new molecule and discuss how model-based methodologies can be applied for the design and analysis of experimental protocols. Issues relative to the scientific rationale are categorised and presented as a hierarchical tree describing the decision-making process. Focus is given to four different areas, namely, optimisation, translation, analytical construct and decision criteria. From a methodological perspective, the relevance of quantitative methods for estimation and extrapolation of risk from toxicology and safety pharmacology experimental protocols, such as points of departure and potency, is discussed in light of advancements in population and Bayesian modelling techniques (e.g. non-linear mixed effects modelling). Their use in the evaluation of pharmacokinetics (PK) and pharmacokinetic-pharmacodynamic relationships (PKPD) has enabled great insight into the dose rationale for medicines in humans, both in terms of efficacy and adverse events. Comparable benefits can be anticipated for the assessment of safety and toxicity profile of novel molecules.

Structured Qualitative Benefit-Risk Assessment to Inform Go/No-Go Decisions for Phase III Testing of Pharmaceutical Products

Late-stage attrition of pharmaceutical products has downsides for both patients participating in clinical trials and sponsor companies. This manuscript presents an outline for a structured qualitative benefit-risk assessment process that sponsors can apply at the time of go/no-go decisions for phase III testing of pharmaceutical products. This approach takes into account the therapeutic and comparator context as well as product-specific information on key benefits, key risks, and risk management. The strengths, limitations, and uncertainties of the evidence are critically evaluated at each step to provide a balanced perspective on the data. Application of a rigorous, structured qualitative benefit-risk assessment to critically evaluate the available information at the time of these decisions lends further support to quality decision making by sponsors. Such an approach ensures that decisions are systematic and transparent and helps development teams communicate the rationale for their recommendations to decision makers. In a succinct organized fashion, this approach provides decision makers with the clinical information they need to ascertain whether pharmaceutical products should be advanced from phase II to phase III testing.

Why is it hard to terminate failing projects in pharmaceutical R&D?

'Quick-kill' strategies in pharmaceutical research and development aim to reduce late-stage attrition by bringing project termination decisions forward, to an earlier point in the process. How can the barriers to implementing such strategies be overcome?

The Acute Extracellular Flux (XF) Assay to Assess Compound Effects on Mitochondrial Function

Numerous investigations have linked mitochondrial dysfunction to adverse health outcomes and drug-induced toxicity. The pharmaceutical industry is challenged with identifying mitochondrial liabilities earlier in drug development and thereby reducing late-stage attrition. Consequently, there is a demand for reliable, higher-throughput screening methods for assessing the impact of drug candidates on mitochondrial function. The extracellular flux (XF) assay described here is a plate-based method in which galactose-conditioned HepG2 cells were acutely exposed to test compounds, then real-time changes in the oxygen consumption rate and extracellular acidification rate were simultaneously measured using a Seahorse Bioscience XF-96 analyzer. The acute XF assay was validated using marketed drugs known to modulate mitochondrial function, and data analysis was automated using a spline curve fitting model developed at GlaxoSmithKline. We demonstrate that the acute XF assay is a robust, sensitive screening platform for evaluating drug-induced effects on mitochondrial activity in whole cells.

Long-term chronic toxicity testing using human pluripotent stem cell-derived hepatocytes.

Human pluripotent stem cells (hPSC) have the potential to become important tools for the establishment of new models for in vitro drug testing of, for example, toxicity and pharmacological effects. Late-stage attrition in the pharmaceutical industry is to a large extent caused by selection of drug candidates using nonpredictive preclinical models that are not clinically relevant. The current hepatic in vivo and in vitro models show clear limitations, especially for studies of chronic hepatotoxicity. For these reasons, we evaluated the potential of using hPSC-derived hepatocytes for long-term exposure to toxic drugs. The differentiated hepatocytes were incubated with hepatotoxic compounds for up to 14 days, using a repeated-dose approach. The hPSC-derived hepatocytes became more sensitive to the toxic compounds after extended exposures and, in addition to conventional cytotoxicity, evidence of phospholipidosis and steatosis was also observed in the cells. This is, to the best of our knowledge, the first report of a long-term toxicity study using hPSC-derived hepatocytes, and the observations support further development and validation of hPSC-based toxicity models for evaluating novel drugs, chemicals, and cosmetics.

A cardiomyocyte-based biosensor for antiarrhythmic drug evaluation by simultaneously monitoring cell growth and beating

Drug-induced cardiotoxicity greatly endangers the human health and results in resource waste. Also, it is a leading attribution to drug withdrawal and late-stage attrition in pharmaceutical industry. In the study, a dual function cardiomyocyte-based biosensor was introduced for rapid drug evaluation with xCELLigence RTCA Cardio system. The cardiomyocyte-based biosensor can monitor the cardiomyocyte growth and beating status simultaneously under the drug effects. Two typical cardiovascular drug, verapamil and flecainide were selected as treatment agents to test the performance of this biosensor. The experiment results showed that the performance of cardiomyocyte-based biosensor verified the basic drug effects by beating status and also tested the drug cytotoxicity by the cell index curves of cardiomyocyte growth. Based on the advanced sensor detection technology and cell culture technology, this cardiomyocyte-based biosensor will be a utility platform for the drug preclinical assessment.

Bacterial mutagenicity screening in the pharmaceutical industry

Genetic toxicity testing is used as an early surrogate for carcinogenicity testing. Genetic toxicity testing is also required by regulatory agencies to be conducted prior to initiation of first in human clinical trials and subsequent marketing for most small molecule pharmaceutical compounds. To reduce the chances of advancing mutagenic pharmaceutical candidates through the drug discovery and development processes, companies have focused on developing testing strategies to maximize hazard identification while minimizing resource expenditure due to late stage attrition. With a large number of testing options, consensus has not been reached on the best mutagenicity platform to use or on the best time to use a specific test to aid in the selection of drug candidates for development. Most companies use a process in which compounds are initially screened for mutagenicity early in drug development using tests that require only a few milligrams of compound and then follow those studies up with a more robust mutagenicity test prior to selecting a compound for full development. This review summarizes the current applications of bacterial mutagenicity assays utilized by pharmaceutical companies in early and late discovery programs. The initial impetus for this review was derived from a workshop on bacterial mutagenicity screening in the pharmaceutical industry presented at the 40th Annual Environmental Mutagen Society Meeting held in St. Louis, MO in October, 2009. However, included in this review are succinct summaries of use and interpretation of genetic toxicity assays, several mutagenicity assays that were not presented at the meeting, and updates to testing strategies resulting in current state-of the art description of best practices. In addition, here we discuss the advantages and liabilities of many broadly used mutagenicity screening platforms and strategies used by pharmaceutical companies. The sensitivity and specificity of these early mutagenicity screening assays using proprietary compounds and their concordance (predictivity) with the regulatory bacterial mutation test are discussed.

Torching the Haystack: modelling fast-fail strategies in drug development

By quickly clearing the development pipeline of failing or marginal products, fast-fail strategies release resources to focus on more promising molecules. The Quick-Kill model of drug development demonstrates that fast-fail strategies will: (1) reduce the expected time to market; (2) reduce expected R&D costs; and (3) increase R&D productivity. This paper outlines the model and demonstrates the impact of fast-fail strategies. The model is illustrated with costs and risks data from pharmaceutical and biopharmaceutical companies.

Target promiscuity and physicochemical properties contribute to pharmacologically induced ER-stress

In vivo toxicity of drug candidates remains a major problem in the pharmaceutical industry, and is a significant cause of late stage attrition. As a consequence predictive in vitro assays are developed and put in place early in the discovery pipeline to aid compound selection. Endoplasmic reticulum stress (ER-stress) has been implicated in many disease states, as well as compound-induced organ toxicities. We explored the role of ER-stress as a general mechanism of toxicity by utilizing a high-throughput in vitro assay to screen 316 chemically diverse Pfizer proprietary compounds with known in vivo toxicity outcome for nuclear accumulation of spliced x-box binding protein 1 (XBP1s), a key transcription factor of the unfolded protein response (UPR). We examined the correlation between physicochemical properties, such as molecular weight, pKA, lipophilicity, topological polar surface area, and passive permeability, as well as target promiscuity, between XBP1s hits and non-hits and found that lipophilicity, target promiscuity and low passive permeability significantly contributed to ER-stress. In addition, we have shown that compounds which cause ER-stress in the form of XBP1s activation at concentrations below 40μM have a more than four times greater chance of causing in vivo toxicity at 10μM plasma exposure.

Evaluation of Cellular Impedance Measures of Cardiomyocyte Cultures for Drug Screening Applications

Cardiovascular toxicity is a leading contributor to drug withdrawal and late-stage attrition. Earlier and broader screening is a validated approach to build-in cardiovascular safety as demonstrated with human Ether-à-go-go-related gene (hERG) screening to reduce drug-induced arrhythmia. There is an urgent need for novel in vitro assays to address other mechanistic aspects of cardiovascular function, including contractility, heart rate, toxicity, hypertrophy, and non-hERG arrhythmia. Recent advances in label-free cellular impedance technology now enable tracking of spontaneous, synchronized beating of cultured cardiomyocytes. Analysis of beating allows integrated detection that is downstream of electrical and mechanical aspects of contraction. Here, we evaluate impedance-based cardiomyocyte responses against criteria required for drug screening. The throughput and sensitivity allowed for rapid assay development. Critical variables for rat neonatal cardiomyocyte assays included cell density and serum levels. Once optimized, consistent, stable beating for at least 3 days was straight-forward to achieve. In tests of compounds spanning a breadth of target classes, the potency values showed excellent precision, wide dynamic range, and consistency across multiple experiments. Cardiomyocyte impedance assays can extract multiple beat-related parameters. In these experiments, rate, amplitude, and rise slope were examined and each yielded acceptable precision. Potency values calculated by beat rate and amplitude were highly correlated for most compounds although selected compounds displayed unique profiles indicative of different mechanisms. Tests with known cardiovascular active drugs revealed concordance with clinical findings. Thus, impedance assays combine novel features including sensitivity to contractile activity, versatile data analysis, and robust/translatable data in a format with sufficient throughput to become a valuable addition to the cardiovascular in vitro screening arsenal.

Concise Review: Human Pluripotent Stem Cell-Based Models for Cardiac and Hepatic Toxicity Assessment

Considering the costs associated with drug development, there are billions of dollars to be saved by reducing late-stage attrition in the pharmaceutical industries. Reports on the use of human pluripotent stem cells (hPSCs) and their functional derivatives in applications for safety assessment of drugs have begun to appear in the scientific literature. These reports are encouraging and fuel further developments of improved human cellular models that may increase the clinical relevance and reduce the need of experimental animals in preclinical drug discovery. However, a few factors still limit the general and wide-spread industry implementation of these new stem cell-based models, including cost of manufacture, level of functionality of the differentiated cells, assay validation, verification of human relevance, and benchmarking to conventional models. This review discusses the emerging field of hPSC-based models for drug discovery and development with a focus on cardiac and hepatic toxicity testing and how these approaches may improve current applications used in the pharmaceutical industry. Although much research remains to make hPSC-based models mainstream tools in the industry, importantly, this review highlights currently available opportunities. In addition, a forward looking discussion on novel applications using tissue preparations generated from hPSCs illustrates the opportunities to create complex models in vitro with the aim of simulating the systemic response of a drug in vivo.

Diversity selection algorithms

AbstractMolecular diversity has been an important topic in chemoinformatics for the last two decades following the introduction of high‐throughput screening and combinatorial chemistry techniques. This article reviews the main algorithms that have been developed for assessing the diversity of a set of compounds and for selecting a diverse subset of compounds from a larger library. Particular focus is given to recent trends including the use of scaffolds as a way of assessing molecular diversity and the importance now given to optimizing multiple properties simultaneously in an attempt to reduce late stage attrition during the drug development stage of drug discovery. © 2011 John Wiley & Sons, Ltd. WIREs Comput Mol Sci 2011 1 580‐589 DOI: 10.1002/wcms.33This article is categorized under: Computer and Information Science > Chemoinformatics

Drug and metabolite concentrations in tissues in relationship to tissue adverse findings: a review

Introduction: Drug blood (or plasma) levels measured during safety preclinical investigations do not always correlate with toxicological findings. Concentrations in target tissues or, even better, at target receptors would probably be more relevant. In addition, toxicity may be caused by drug metabolites which, in turn, can be tissue specific. Tissue concentrations and tissue metabolism may be crucial for interpreting tissue toxicity.Areas covered: This paper, starting from the authors' direct experience, focuses on distribution of the parent compound and metabolites in target toxicity tissues and presents a review of several examples where organ or tissue concentrations have been either useful or not relevant for interpreting safety findings. Regulatory aspects and technological progresses are also mentioned.Expert opinion: The authors advocate directing more attention and efforts toward investigating tissue distribution: this approach might reduce late stage attrition. When unexpected tissue toxicity is found, measuring drug concentrations in the target tissue and characterising and measuring tissue metabolites could bring relevant information for interpreting the adverse finding. Evidence of slow accumulation of a long lasting metabolite in a tissue should be considered as an alert: this evidence can be obtained during short-term toxicity studies.

Modeling Bone Marrow Toxicity Using Kinase Structural Motifs and the Inhibition Profiles of Small Molecular Kinase Inhibitors

The cellular function of kinases combined with the difficulty of designing selective small molecule kinase inhibitors (SMKIs) poses a challenge for drug development. The late-stage attrition of SMKIs could be lessened by integrating safety information of kinases into the lead optimization stage of drug development. Herein, a mathematical model to predict bone marrow toxicity (BMT) is presented which enables the rational design of SMKIs away from this safety liability. A specific example highlights how this model identifies critical structural modifications to avoid BMT. The model was built using a novel algorithm, which selects 19 representative kinases from a panel of 277 based upon their ATP-binding pocket sequences and ability to predict BMT in vivo for 48 SMKIs. A support vector machine classifier was trained on the selected kinases and accurately predicts BMT with 74% accuracy. The model provides an efficient method for understanding SMKI-induced in vivo BMT earlier in drug discovery.

Pharmacophore models for GABAA modulators: implications in CNS drug discovery

Importance of the field: GABAA ion channel is a validated drug target, implicated in the pathophysiology of various neurological and psychiatric disorders. Structural investigations on GABAA are currently precluded in the absence of experimentally resolved structure. Pharmacophore modeling circumvents such issues and proves to be a powerful and successful method in drug discovery.Areas covered in this review: The present reviews encompass pharmacophoric models available in the literature for the orthosteric GABA and the allosteric benzodiazepine binding site. Success stories from these simplistic pharmacophore models in scaffold hopping and strategic lead optimization have been highlighted. Recent advances in pharmacophore modeling that can leverage CNS drug discovery programs and deliver astounding results have been reviewed.What the reader will gain: Readers are bound to gain a comprehensive insight on different computational techniques used by different groups to arrive at simple, yet sophisticated pharmacophore models. In the absence of experimentally unresolved active site geometry of GABAA, these models will provide the reader an opportunity to translate these pharmacophoric features to the microscopic phenomenon of supramolecular ligand interaction.Take home message: Pharmacophore modeling has now evolved as a mainstay approach for lead generation and optimization in drug discovery programs. Of late, many advances in pharmacophore perception have emerged. Such advancements should be used to confront activity profiling and early stage risk assessment in a high-throughput fashion. Extending such technologies has the potential not only to reduce time and cost, but also to prevent late stage attrition in drug discovery.

Building a Tiered Approach to In Vitro Predictive Toxicity Screening: A Focus on Assays with In Vivo Relevance

One of the greatest challenges facing the pharmaceutical industry today is the failure of promising new drug candidates due to unanticipated adverse effects discovered during preclinical animal safety studies and clinical trials. Late stage attrition increases the time required to bring a new drug to market, inflates development costs, and represents a major source of inefficiency in the drug discovery/development process. It is generally recognized that early evaluation of new drug candidates is necessary to improve the process. Building in vitro data sets that can accurately predict adverse effects in vivo would allow compounds with high risk profiles to be deprioritized, while those that possess the requisite drug attributes and a lower risk profile are brought forward. In vitro cytotoxicity assays have been used for decades as a tool to understand hypotheses driven questions regarding mechanisms of toxicity. However, when used in a prospective manner, they have not been highly predictive of in vivo toxicity. Therefore, the issue may not be how to collect in vitro toxicity data, but rather how to translate in vitro toxicity data into meaningful in vivo effects. This review will focus on the development of an in vitro toxicity screening strategy that is based on a tiered approach to data collection combined with data interpretation.

Human stem cells for modeling neurological disorders: Accelerating the drug discovery pipeline

The availability of human stem cells heralds a new era for modeling normal and pathologic tissues and developing therapeutics. For example, the in vitro recapitulation of normal and aberrant neurogenesis holds significant promise as a tool for de novo modeling of neurodevelopmental and neurodegenerative diseases. Translational applications include deciphering brain development, function, pathologies, traditional medications, and drug discovery for novel pharmacotherapeutics. For the latter, human stem cell-based assays represent a physiologically relevant and high-throughput means to assess toxicity and other undesirable effects early in the drug development pipeline, avoiding late-stage attrition whilst expediting proof-of-concept of genuine drug candidates. Here we consider the potential of human embryonic, adult, and induced pluripotent stem cells for studying neurological disorders and preclinical drug development.

Analysis of Pharmacology Data and the Prediction of Adverse Drug Reactions and Off‐Target Effects from Chemical Structure

Preclinical Safety Pharmacology (PSP) attempts to anticipate adverse drug reactions (ADRs) during early phases of drug discovery by testing compounds in simple, in vitro binding assays (that is, preclinical profiling). The selection of PSP targets is based largely on circumstantial evidence of their contribution to known clinical ADRs, inferred from findings in clinical trials, animal experiments, and molecular studies going back more than forty years. In this work we explore PSP chemical space and its relevance for the prediction of adverse drug reactions. Firstly, in silico (computational) Bayesian models for 70 PSP-related targets were built, which are able to detect 93% of the ligands binding at IC(50) < or = 10 microM at an overall correct classification rate of about 94%. Secondly, employing the World Drug Index (WDI), a model for adverse drug reactions was built directly based on normalized side-effect annotations in the WDI, which does not require any underlying functional knowledge. This is, to our knowledge, the first attempt to predict adverse drug reactions across hundreds of categories from chemical structure alone. On average 90% of the adverse drug reactions observed with known, clinically used compounds were detected, an overall correct classification rate of 92%. Drugs withdrawn from the market (Rapacuronium, Suprofen) were tested in the model and their predicted ADRs align well with known ADRs. The analysis was repeated for acetylsalicylic acid and Benperidol which are still on the market. Importantly, features of the models are interpretable and back-projectable to chemical structure, raising the possibility of rationally engineering out adverse effects. By combining PSP and ADR models new hypotheses linking targets and adverse effects can be proposed and examples for the opioid mu and the muscarinic M2 receptors, as well as for cyclooxygenase-1 are presented. It is hoped that the generation of predictive models for adverse drug reactions is able to help support early SAR to accelerate drug discovery and decrease late stage attrition in drug discovery projects. In addition, models such as the ones presented here can be used for compound profiling in all development stages.

Strategies to reduce late-stage drug attrition due to mitochondrial toxicity

Mitochondrial dysfunction is increasingly implicated in the etiology of drug-induced toxicities and negative side-effect profiles. Early identification of mitochondrial liabilities for new chemical entities is therefore crucial for avoiding late-stage attrition during drug development. Limitations of traditional methods for assessing mitochondrial dysfunction have discouraged routine evaluation of mitochondrial liabilities. To circumvent this bottleneck, a high-throughput screen has been developed that measures oxygen consumption; one of the most informative parameters for the assessment of mitochondrial status. This technique has revealed that some, but not all, members of many major drug classes have mitochondrial liabilities. This dichotomy encourages optimism that efficacy can be disassociated from mitochondrial toxicity, resulting in safer drugs in the future.

Is it Time to Revisit the Current R&amp;D Model?

factor in the transformation of the course and treatment of disease over the past half century. It is estimated that over 90% of the medicines in use today have been discovered or developed by the industry. Successful medicines bring revenues to the companies who research and develop them through long-term at-risk investment; the ensuing returns through medicine sales are a key element in sustaining a virtuous circle in the traditional drug research and development (R&D) model. However, late-stage attrition, along with increasing development costs, are now challenging companies and the current R&D model. As a consequence, biopharmaceutical companies are under ever increasing pressure to develop new R&D models. This article describes the current status of large pharma R&D and how it goes about developing new drugs. It analyses some of the current key industry strategic trends and themes that may govern whether the current business model succeeds and continues to be the engine house of delivery for new medicines for society, or whether key changes are required to maximise R&D efficiency and to deliver regulatory approvable and medically required new drugs.