Computational modeling in melanoma for novel drug discovery

作     者：Pennisi, Marzio Russo, Giulia Di Salvatore, Valentina Candido, Saverio Libra, Massimo Pappalardo, Francesco 

作者机构：Univ Catania Dept Math & Comp Sci I-95125 Catania Italy Univ Catania Dept Biomed & Biotechnol Sci I-95125 Catania Italy CNR Inst Neurol Sci Catania Italy Univ Catania Dept Drug Sci Vle A Doria 6 I-95125 Catania Italy 

出 版 物：《EXPERT OPINION ON DRUG DISCOVERY》 (药物发明专家评论)

年 卷 期：2016年第11卷第6期

页      面：609-621页

核心收录：

学科分类：1007[医学-药学(可授医学、理学学位)] 10[医学] 

基　　金：Finanziamento Ricerca di Ateneo (FIR) from the Universita degli Studi di Catania Lega Italiana per la Lotta contro i Tumori, Ricerca Sanitaria 

主　　题：Computational modeling in silico drug discovery melanoma systems biology 

摘      要：Introduction: There is a growing body of evidence highlighting the applications of computational modeling in the field of biomedicine. It has recently been applied to the in silico analysis of cancer dynamics. In the era of precision medicine, this analysis may allow the discovery of new molecular targets useful for the design of novel therapies and for overcoming resistance to anticancer drugs. According to its molecular behavior, melanoma represents an interesting tumor model in which computational modeling can be applied. Melanoma is an aggressive tumor of the skin with a poor prognosis for patients with advanced disease as it is resistant to current therapeutic approaches. Areas covered: This review discusses the basics of computational modeling in melanoma drug discovery and development. Discussion includes the in silico discovery of novel molecular drug targets, the optimization of immunotherapies and personalized medicine trials. Expert opinion: Mathematical and computational models are gradually being used to help understand biomedical data produced by high-throughput analysis. The use of advanced computer models allowing the simulation of complex biological processes provides hypotheses and supports experimental design. The research in fighting aggressive cancers, such as melanoma, is making great strides. Computational models represent the key component to complement these efforts. Due to the combinatorial complexity of new drug discovery, a systematic approach based only on experimentation is not possible. Computational and mathematical models are necessary for bringing cancer drug discovery into the era of omics, big data and personalized medicine.