Requirements for a lead compound to become a clinical candidate

A drug candidate suitable for clinical testing is expected to bind selectively to the receptor site on the target, to elicit the desired functional response of the target molecule, and to have adequate bioavailability and biodistribution to elicit the desired responses in animals and humans; it must also pass formal toxicity evaluation in animals. The path from lead to clinical drug candidate represents the most idiosyncratic segment of drug discovery and development. Each program is unique and setbacks are common, making it difficult to predict accurately the duration or costs of this segment. Because of incidents of unpredicted human toxicity seen in recent years, the regulatory agencies and public demands for safety of new drug candidates have become very strict, and safety issues are dominant when identifying a clinical drug candidate

Novel transcripts reveal a complex structure of the human TRKA gene and imply the presence of multiple protein isoforms

Publisher Copyright: © 2015 Luberg et al.Background: Tropomyosin-related kinase A (TRKA) is a nerve growth factor (NGF) receptor that belongs to the tyrosine kinase receptor family. It is critical for the correct development of many types of neurons including pain-mediating sensory neurons and also controls proliferation, differentiation and survival of many neuronal and non-neuronal cells. TRKA (also known as NTRK1) gene is a target of alternative splicing which can result in several different protein isoforms. Presently, three human isoforms (TRKAI, TRKAII and TRKAIII) and two rat isoforms (TRKA L0 and TRKA L1) have been described. Results: We show here that human TRKA gene is overlapped by two genes and spans 67 kb-almost three times the size that has been previously described. Numerous transcription initiation sites from eight different 5' exons and a sophisticated splicing pattern among exons encoding the extracellular part of TRKA receptor indicate that there might be a large variety of alternative protein isoforms. TrkA genes in rat and mouse appear to be considerably shorter, are not overlapped by other genes and display more straightforward splicing patterns. We describe the expression profile of alternatively spliced TRKA transcripts in different tissues of human, rat and mouse, as well as analyze putative endogenous TRKA protein isoforms in human SH-SY5Y and rat PC12 cells. We also characterize a selection of novel putative protein isoforms by portraying their phosphorylation, glycosylation and intracellular localization patterns. Our findings show that an isoform comprising mainly of TRKA kinase domain is capable of entering the nucleus. Conclusions: Results obtained in this study refer to the existence of a multitude of TRKA mRNA and protein isoforms, with some putative proteins possessing very distinct properties.publishersversionPeer reviewe

Single Cycle Structure-Based Humanization of an Anti-Nerve Growth Factor Therapeutic Antibody

Most forms of chronic pain are inadequately treated by present therapeutic options. Compelling evidence has accumulated, demonstrating that Nerve Growth Factor (NGF) is a key modulator of inflammatory and nociceptive responses, and is a promising target for the treatment of human pathologies linked to chronic and inflammatory pain. There is therefore a growing interest in the development of therapeutic molecules antagonising the NGF pathway and its nociceptor sensitization actions, among which function-blocking anti-NGF antibodies are particularly relevant candidates

Polypeptide and Protein Modeling for Drug Design

The main pathways involved in pain processing have been known for some time, but the precise microcircuitry remains surprisingly unclear. This has allowed very different theories of pain processing to persist. Specificity theory holds that pain is qualitatively distinct from other somatosensory percepts and that the underlying circuitry is arranged as labeled lines. Gate control theory holds that all inputs converge and that it is the level of activation in unspecialized neurons that code for pain. The truth lies somewhere in between. The dorsal horn of the spinal cord, which corresponds to the first synaptic relay point, comprises a diverse set of interneurons whose connectivity is only partially worked out. This lack of data has hindered network-level modeling, but this also presents an opportunity for modeling to help guide future experiments

Classification and analysis of a large collection of <i>in vivo</i> bioassay descriptions

<div><p>Testing potential drug treatments in animal disease models is a decisive step of all preclinical drug discovery programs. Yet, despite the importance of such experiments for translational medicine, there have been relatively few efforts to comprehensively and consistently analyze the data produced by <i>in vivo</i> bioassays. This is partly due to their complexity and lack of accepted reporting standards—publicly available animal screening data are only accessible in unstructured free-text format, which hinders computational analysis. In this study, we use text mining to extract information from the descriptions of over 100,000 drug screening-related assays in rats and mice. We retrieve our dataset from ChEMBL—an open-source literature-based database focused on preclinical drug discovery. We show that <i>in vivo</i> assay descriptions can be effectively mined for relevant information, including experimental factors that might influence the outcome and reproducibility of animal research: genetic strains, experimental treatments, and phenotypic readouts used in the experiments. We further systematize extracted information using unsupervised language model (Word2Vec), which learns semantic similarities between terms and phrases, allowing identification of related animal models and classification of entire assay descriptions. In addition, we show that random forest models trained on features generated by Word2Vec can predict the class of drugs tested in different <i>in vivo</i> assays with high accuracy. Finally, we combine information mined from text with curated annotations stored in ChEMBL to investigate the patterns of usage of different animal models across a range of experiments, drug classes, and disease areas.</p></div