Current Medicinal Chemistry - Volume 24, Issue 23, 2017

Background: Calculation of ligand-binding affinity is an open problem in computational medicinal chemistry. The ability to computationally predict affinities has a beneficial impact in the early stages of drug development, since it allows a mathematical model to assess protein-ligand interactions. Due to the availability of structural and binding information, machine learning methods have been applied to generate scoring functions with good predictive power. Objective: Our goal here is to review recent developments in the application of machine learning methods to predict ligand-binding affinity. Method: We focus our review on the application of computational methods to predict binding affinity for protein targets. In addition, we also describe the major available databases for experimental binding constants and protein structures. Furthermore, we explain the most successful methods to evaluate the predictive power of scoring functions. Results: Association of structural information with ligand-binding affinity makes it possible to generate scoring functions targeted to a specific biological system. Through regression analysis, this data can be used as a base to generate mathematical models to predict ligandbinding affinities, such as inhibition constant, dissociation constant and binding energy. Conclusion: Experimental biophysical techniques were able to determine the structures of over 120,000 macromolecules. Considering also the evolution of binding affinity information, we may say that we have a promising scenario for development of scoring functions, making use of machine learning techniques. Recent developments in this area indicate that building scoring functions targeted to the biological systems of interest shows superior predictive performance, when compared with other approaches.

Disrupted kynurenine pathway (KP) metabolism has been implicated in the progression of neurodegenerative disease, psychiatric disorders and cancer. Modulation of enzyme activity along this pathway may therefore offer potential new therapeutic strategies for these conditions. Considering their prominent positions in the KP, the enzymes indoleamine 2,3-dioxygenase, kynurenine 3-monooxygenase and kynurenine aminotransferase, appear the most attractive targets. Already, increasing interest in this pathway has led to the identification of a number of potent and selective enzyme inhibitors with promising pre-clinical data and the elucidation of several enzyme crystal structures provides scope to rationalize the molecular mechanisms of inhibitor activity. The field seems poised to yield one or more inhibitors that should find clinical utility.

Background: In the past few years, great of attention has been paid to the identification and characterization of selective and potent inhibitors of the first identified histone demethylase LSD1, which may erase mono- and di-methylated histone 3 lysine 4 and 9. As the aberrant overexpression of LSD1 is involved in various pathological processes, especially cancer, obtaining selective and potent LSD1 inhibitors has emerged as a crucial issue in medicinal chemistry research. Method: Until now, several LSD1 inhibitor screening models have been established, including enzyme coupled assay, LC-MS based assay, and FRET based assay. Nevertheless, due to some special instrument requirement and additional costs of LC-MS and FRET, the enzyme coupled assay is the most widely applied method for LSD1 inhibitor screening. Result: We summarized and compared several reported in vitro LSD1 inhibitor screening models. Each of them has distinct advantages and disadvantages, and none of these methods is perfect. In order to exclude the false positive results, at least one additional method should be applied to screen LSD1 inhibitors.

Breast cancer is the most common cancer in women, with roughly half a million deaths per year worldwide. Among various approaches for breast cancer treatment, chemotherapy is predominantly used for patients at stages II-IV, and monoclonal antibody (mAb) therapy is used for patients with human epidermal growth factor receptor 2 (HER2) overexpression. Integrating the tumor specificity provided by unique mAbs and cytotoxicity of small molecule drugs, antibody-drug conjugates (ADCs) are a series of smart chemotherapeutics that have recently shown great promise in treating a number of cancer types. ADCs are designed to selectively attack and kill cancer cells with minimal toxicity to normal tissues. Ado-Trastuzumab emtansine (T-DM1) was the first and only ADC approved by the US Food and Drug Administration for HER2-positive breast cancer. Following the success of T-DM1, many novel ADCs have been developed, and their anticancer efficacies are currently undergoing preclinical or clinical investigation. The development of ADCs is a rapidly progressing field, and this review aims to summarize the most recent advances in ADCs targeting breast cancer over the past five years (2011-2016). The review highlights compositions and mechanisms of action of these newly developed ADCs and discusses current challenges and future directions of developing new ADCs for improved treatment of breast cancer.

Background: The term Alcohol Use Disorder (AUD) incorporates different states of disease related to the recurrent use of alcohol and linked to the relevant impairment, disability and failure to perform major responsibilities in different realms. Many neurotransmitter systems are involved in the phases or states of alcoholism from reward mechanisms, associated to binge intoxication, to stress and anxiety linked to relapse and withdrawal. Some neuropeptides play a key function in the control of anxiety and stress, and establish a close relationship with the pathological mechanisms underlying alcohol addiction. Among them, Neuropeptide Y (NPY), Corticotropin-releasing factor (CRF)/Urocortins and Neuropeptide S (NPS) cross-talk, and are responsible for some of the maladaptation processes that the brain exhibits during the progression of the disease. Method: In this study, we review the literature mainly focused on the participation of these neuropeptides in the pathophysiology of AUD, as well as on the use of antagonists designed to investigate signaling mechanisms initiated after ligand binding and their connection to biochemical adaptation events coupled to alcohol addiction. The possibility that these systems may serve as therapeutic objectives to mitigate or eliminate the harm that drinking ethanol generates, is also discussed. Conclusion: The peptide systems reviewed here, together with other neurotransmitter systems and their mutual relationships, are firm candidates to be targeted to treat AUD.