Attrition Rate Of Compounds Research Articles

Array of translational systems pharmacodynamic models of anti-cancer drugs.

Cancer is a complex disease that is characterized by an uncontrolled growth and spread of abnormal cells. Drug development in oncology is particularly challenging and is associated with one of the highest attrition rates of compounds despite substantial investments in resources. Pharmacokinetic and pharmacodynamic (PK/PD) modeling seeks to couple experimental data with mathematical models to provide key insights into factors controlling cytotoxic effects of chemotherapeutics and cancer progression. PK/PD modeling of anti-cancer compounds is equally challenging, partly based on the complexity of biological and pharmacological systems. However, reliable mechanistic and systems PK/PD models for anti-cancer agents have been developed and successfully applied to: (1) provide insights into fundamental mechanisms implicated in tumor growth, (2) assist in dose selection for first-in-human phase I studies (e.g., effective dose, escalating doses, and maximal tolerated doses), (3) design and optimize combination drug regimens, (4) design clinical trials, and (5) establish links between drug efficacy and safety and the concentrations of measured biomarkers. In this commentary, classes of relevant mechanism-based and systems PK/PD models of anti-cancer agents that have shown promise in translating preclinical data and enhancing stages of the drug development process are reviewed. Specific features of such models are discussed including their strengths and limitations along with a prospectus of using these models alone or in combination for cancer therapy.

Impact of Computational Structure-Based Predictive Toxicology in Drug Discovery

Computational tools for predicting toxicity have been envisioned to have the potential to broadly impact up on the attrition rate of compounds in pre-clinical drug discovery and development. An integrated approach of computer-assisted, predictive, and physico-chemical properties of a compound, along with its in vitro and in vivo analysis, needs to be routinely exercised in the lead identification and lead optimization processes. Starting with a good lead can save a lot of money and it can significantly reduce the entire drug discovery process. The journey towards triple R's- reduce, replace and refine, further proves to be successful in predicting adverse drug reactions in patients (or animals) enrolled in clinical trials. However, the impact of predictive toxicity analysis was modest and relatively narrow in scope, due to the limited domain knowledge in this field. It is important to note that advances within medical science and newer approaches in drug development will require predictive toxicology applications to be viable. The field of computational toxicology has been heading in a direction more relevant to human diseases by reducing the adverse drug reactions. Therefore, efforts must be directed to integrating these tools relevant to the goal of preventing undesired toxicity in pre-clinical trials followed by different phases of clinical trials.

Abstract A25: Establishment and characterization of primary human Biomerk Tumorgraft™ Models: Application to oncology drug development

Abstract One of the key challenges facing oncology drug development is the high attrition rates of compounds that enter the drug development pipeline, where very few achieve successful approval and marketing. Champions Biotechnology, in an effort to enhance the value of preclinical compounds and accelerate oncology drug development, has developed a novel preclinical platform derived from Biomerk Tumorgraft™ models; an innovative approach that utilizes the implantation of primary human tumors in immune-deficient mice in a manner that preserves the biological properties of the original human tumor. Biomerk Tumorgrafts™ differ from traditional xenograft models in that they are not passaged in tissue culture, and are instead exclusively passaged in vivo. Additionally, the Biomerk Tumorgraft™ models: (a) maintain the fundamental genotypic features of the original cancer, (b) represent the genetic heterogeneity of the cancer, and (c) do not change over several passages. Champions has established a bank of over 200 Tumorgraft models encompassing all major solid tumor types, all of which have been extensively characterized. Here we describe a panel of NSCLC lung, colon, and pancreatic tumorgraft models including gene expression profiling, mutational analyses and responses to standard of care agents. Of the NSCLC lung models, 6/8 demonstrated resistance to Erlotinib with 2/8 demonstrating an intermediate response, 2/6 also possessed a mutant K-ras or a mutant EGFR. Similarly, 3/6 K-ras mutant colon Tumorgraft models demonstrated sensitivity to irinotecan. In the case of the pancreatic models, 7/8 showed resistance to Erlotinib with the remaining model showing only a modest response. Through Champions' Personalized Oncology Services, we have also shown a high correlation between the Tumorgraft response and the clinical response. Together, these results demonstrate that each model differs with respect to its genetic composition and response to standard agents, and thus represents the efficacy of a compound on an individual patient. Given the diversity of models, the maintenance of the genomic and phenotypic characteristics of the original patient tumor, and the correlation between Tumorgraft and clinical responses, Champions Biomerk Tumorgraft™ models represent a novel preclinical in vivo platform capable of optimizing drug regimens for cancer patients and predicting the clinical effectiveness of preclinical drug candidates. Overall, the application of Biomerk Tumorgraft™ models has the potential to accelerate and enhance oncology drug development. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):A25.