New Paradigm in Treating Cancer: Right on Target.

Cancer prevalence is increasing every year and now cancer is the third highest cause of death in developing countries. Effective anticancer treatment can prolong life and improve the patient's quality of life. Targeted therapy is a new therapeutic modality which targets specific molecules in the cancer cell and disrupts dysregulated signaling pathways involved in carcinogenesis. Since targeted therapy does not attack normal cells, its side effects are considered low compared to chemotherapy. More than 15 drugs have been approved for treatment in various human cancers. These drugs can largely be grouped into tyrosine kinase inhibitors and monoclonal antibodies. This review will focus on the most common agents within both groups.

Analysis of A Drug Target-based Classification System using Molecular Descriptors.

Drug-target interaction is an important topic in drug discovery and drug repositioning. KEGG database offers a drug annotation and classification using a target-based classification system. In this study, we gave an investigation on five target-based classes: (I) G protein-coupled receptors; (II) Nuclear receptors; (III) Ion channels; (IV) Enzymes; (V) Pathogens, using molecular descriptors to represent each drug compound. Two popular feature selection methods, maximum relevance minimum redundancy and incremental feature selection, were adopted to extract the important descriptors. Meanwhile, an optimal prediction model based on nearest neighbor algorithm was constructed, which got the best result in identifying drug target-based classes. Finally, some key descriptors were discussed to uncover their important roles in the identification of drug-target classes.

Large-scale automatic extraction of side effects associated with targeted anticancer drugs from full-text oncological articles.

Targeted anticancer drugs such as imatinib, trastuzumab and erlotinib dramatically improved treatment outcomes in cancer patients, however, these innovative agents are often associated with unexpected side effects. The pathophysiological mechanisms underlying these side effects are not well understood. The availability of a comprehensive knowledge base of side effects associated with targeted anticancer drugs has the potential to illuminate complex pathways underlying toxicities induced by these innovative drugs. While side effect association knowledge for targeted drugs exists in multiple heterogeneous data sources, published full-text oncological articles represent an important source of pivotal, investigational, and even failed trials in a variety of patient populations. In this study, we present an automatic process to extract targeted anticancer drug-associated side effects (drug-SE pairs) from a large number of high profile full-text oncological articles. We downloaded 13,855 full-text articles from the Journal of Oncology (JCO) published between 1983 and 2013. We developed text classification, relationship extraction, signaling filtering, and signal prioritization algorithms to extract drug-SE pairs from downloaded articles. We extracted a total of 26,264 drug-SE pairs with an average precision of 0.405, a recall of 0.899, and an F1 score of 0.465. We show that side effect knowledge from JCO articles is largely complementary to that from the US Food and Drug Administration (FDA) drug labels. Through integrative correlation analysis, we show that targeted drug-associated side effects positively correlate with their gene targets and disease indications. In conclusion, this unique database that we built from a large number of high-profile oncological articles could facilitate the development of computational models to understand toxic effects associated with targeted anticancer drugs.

Target selection for FDA-approved medicines.

The biopharmaceutical industry translates fundamental understanding of disease into new medicines. As part of a comprehensive analysis of FDA-approved new molecular entities (NMEs), we assessed the mechanistic basis of drug efficacy, with emphasis on target selection. Three target families capture almost half of all NMEs and the leading ten families capture more than three-quarters of NME approvals. Target families were related to their clinical application and identify dynamic trends in targeting over time. These data suggest increasing attention toward novel target families, which presumably reflects increased understanding of disease etiology. We also suggest the need to balance the ongoing emphasis on target-based drug discovery with phenotypic approaches to drug discovery.

[Biotherapies, immunotherapies, targeted therapies, biopharmaceuticals which word should be used?]. / Biothérapies, immunothérapies, thérapies ciblées, biomédicaments - De quoi faut-il parler ?

The ability to accurately describe and name medical advances is a prerequisite to foster public debates with scientists and physicians, and favour faith over fear among patients and citizens. Therapeutic antibodies are a good example of a medical breakthrough which has met with considerable clinical success, and which terminology has changed over the years. If the appellation serotherapy was appropriate a century ago, it has become obsolete. Recent names such as biotherapy, immunotherapy, targeted therapy, biopharmaceuticals have been introduced and are now commonly used, each of those can apply to therapeutic antibodies. It is thus interesting to question the real meaning of these different appellations. Our goal in this manuscript is to analyse the genesis of these terms but also to suggest how to simplify the terminology: biotherapy or targeted therapy need to be eliminated, as well as immunotherapy when communicating with non scientific public. It is recommended to favour the term biopharmaceuticals (biomédicaments in French), which clearly indicates the origin of these molecules, intermediate between chemical drugs and living biologics, whose borders need to be accurately defined also.

Clinical classification of targeted agents used for anticancer treatment.

The introduction of targeted agents has resulted in a breakthrough in advanced cancer treatment. We propose a new classification for these agents to evaluate them in appropriate clinical trials according to agent class. Class I agents that inhibit driver oncogene activities result in massive and rapid tumor shrinkage, with response rates as high as 70% when administered to patients with appropriate targets. These agents can be evaluated in single-arm phase II trials with response rate as the primary endpoint. Class II agents inhibit one oncogene that is partially responsible for accelerating tumor cell proliferation. Their clinical features include synergism with cytotoxic agents and moderate single-agent activity, as shown by response rates of between 10% and 30%. Randomized phase II trials in patients with over-expressed targets are appropriate for the evaluation of these agents. Class III agents inhibit proliferation regulators that are not always oncogenic. Their clinical activity is unique, as they confer a survival benefit on patients with a minimum tumor shrinkage effect. Class IV agents target environmental molecules that act on normal cells surrounding tumor cells, such as the endothelial cells that form vessels. Placebo-controlled randomized phase II trials are required to identify the clinical activities of both class III and IV agents. Class V agents act by enhancing anti-tumor immunity. Immune-related response criteria should aid the evaluation of these agents. We believe that this classification for targeted agents should facilitate their further clinical development.