Drug repurposing: Iron in the fire for older drugs.

Repositioning or "repurposing" of existing therapies for indications of alternative disease is an attractive approach that can generate lower costs and require a shorter approval time than developing a de novo drug. The development of experimental drugs is time-consuming, expensive, and limited to a fairly small number of targets. The incorporation of separate and complementary data should be used, as each type of data set exposes a specific feature of organism knowledge Drug repurposing opportunities are often focused on sporadic findings or on time-consuming pre-clinical drug tests which are often not guided by hypothesis. In comparison, repurposing in-silico drugs is a new, hypothesis-driven method that takes advantage of big-data use. Nonetheless, the widespread use of omics technology, enhanced data storage, data sense, machine learning algorithms, and computational modeling all give unparalleled knowledge of the methods of action of biological processes and drugs, providing wide availability, for both disease-related data and drug-related data. This review has taken an in-depth look at the current state, possibilities, and limitations of further progress in the field of drug repositioning.

COMBSecretomics: A pragmatic methodological framework for higher-order drug combination analysis using secretomics.

Multi drug treatments are increasingly used in the clinic to combat complex and co-occurring diseases. However, most drug combination discovery efforts today are mainly focused on anticancer therapy and rarely examine the potential of using more than two drugs simultaneously. Moreover, there is currently no reported methodology for performing second- and higher-order drug combination analysis of secretomic patterns, meaning protein concentration profiles released by the cells. Here, we introduce COMBSecretomics (https://github.com/EffieChantzi/COMBSecretomics.git), the first pragmatic methodological framework designed to search exhaustively for second- and higher-order mixtures of candidate treatments that can modify, or even reverse malfunctioning secretomic patterns of human cells. This framework comes with two novel model-free combination analysis methods; a tailor-made generalization of the highest single agent principle and a data mining approach based on top-down hierarchical clustering. Quality control procedures to eliminate outliers and non-parametric statistics to quantify uncertainty in the results obtained are also included. COMBSecretomics is based on a standardized reproducible format and could be employed with any experimental platform that provides the required protein release data. Its practical use and functionality are demonstrated by means of a proof-of-principle pharmacological study related to cartilage degradation. COMBSecretomics is the first methodological framework reported to enable secretome-related second- and higher-order drug combination analysis. It could be used in drug discovery and development projects, clinical practice, as well as basic biological understanding of the largely unexplored changes in cell-cell communication that occurs due to disease and/or associated pharmacological treatment conditions.

Revealing cytotoxic substructures in molecules using deep learning.

In drug development, late stage toxicity issues of a compound are the main cause of failure in clinical trials. In silico methods are therefore of high importance to guide the early design process to reduce time, costs and animal testing. Technical advances and the ever growing amount of available toxicity data enabled machine learning, especially neural networks, to impact the field of predictive toxicology. In this study, cytotoxicity prediction, one of the earliest handles in drug discovery, is investigated using a deep learning approach trained on a highly consistent in-house data set of over 34,000 compounds with a share of less than 5% of cytotoxic molecules. The model reached a balanced accuracy of over 70%, similar to previously reported studies using Random Forest. Albeit yielding good results, neural networks are often described as a black box lacking deeper mechanistic understanding of the underlying model. To overcome this absence of interpretability, a Deep Taylor Decomposition method is investigated to identify substructures that may be responsible for the cytotoxic effects, the so-called toxicophores. Furthermore, this study introduces cytotoxicity maps which provide a visual structural interpretation of the relevance of these substructures. Using this approach could be helpful in drug development to predict the potential toxicity of a compound as well as to generate new insights into the toxic mechanism. Moreover, it could also help to de-risk and optimize compounds.

The Pharmaceutical Industry in 2019. An Analysis of FDA Drug Approvals from the Perspective of Molecules.

During 2019, the US Food and Drug Administration (FDA) approved 48 new drugs (38 New Chemical Entities and 10 Biologics). Although this figure is slightly lower than that registered in 2018 (59 divided between 42 New Chemical Entities and 17 Biologics), a year that broke a record with respect to new drugs approved by this agency, it builds on the trend initiated in 2017, when 46 drugs were approved. Of note, three antibody drug conjugates, three peptides, and two oligonucleotides were approved in 2019. This report analyzes the 48 new drugs of the class of 2019 from a strictly chemical perspective. The classification, which was carried out on the basis of chemical structure, includes the following: Biologics (antibody drug conjugates, antibodies, and proteins); TIDES (peptide and oligonucleotides); drug combinations; natural products; and small molecules.

Modeling Tumor Growth and Treatment Resistance Dynamics Characterizes Different Response to Gefitinib or Chemotherapy in Non-Small Cell Lung Cancer.

Differences in the effect of gefitinib and chemotherapy on tumor burden in non-small cell lung cancer remain to be fully understood. Using a Bayesian hierarchical model of tumor size dynamics, we estimated the rates of tumor growth and treatment resistance for patients in the Iressa Pan-Asia Study study (NCT00322452). The following relationships characterize greater efficacy of gefitinib in epidermal growth factor receptor (EGFR) positive tumors: Maximum drug effect is, in decreasing order, gefitinib in EGFR-positive, chemotherapy in EGFR-positive, chemotherapy in EGFR-negative, and gefitinib in EGFR-negative tumors; the rate of resistance emergence is, in increasing order: gefitinib in EGFR positive, chemotherapy in EGFR positive, while each is plausibly similar to the rate in EGFR negative tumors, which are estimated with less certainty. The rate of growth is smaller in EGFR-positive than in EGFR-negative fully resistant tumors, regardless of treatment. The model can be used to compare treatment effects and resistance dynamics among different drugs.

BRADSHAW: a system for automated molecular design.

This paper introduces BRADSHAW (Biological Response Analysis and Design System using an Heterogenous, Automated Workflow), a system for automated molecular design which integrates methods for chemical structure generation, experimental design, active learning and cheminformatics tools. The simple user interface is designed to facilitate access to large scale automated design whilst minimising software development required to introduce new algorithms, a critical requirement in what is a very fast moving field. The system embodies a philosophy of automation, best practice, experimental design and the use of both traditional cheminformatics and modern machine learning algorithms.

Focused Library Generator: case of Mdmx inhibitors.

We present a Focused Library Generator that is able to create from scratch new molecules with desired properties. After training the Generator on the ChEMBL database, transfer learning was used to switch the generator to producing new Mdmx inhibitors that are a promising class of anticancer drugs. Lilly medicinal chemistry filters, molecular docking, and a QSAR IC50 model were used to refine the output of the Generator. Pharmacophore screening and molecular dynamics (MD) simulations were then used to further select putative ligands. Finally, we identified five promising hits with equivalent or even better predicted binding free energies and IC50 values than known Mdmx inhibitors. The source code of the project is available on https://github.com/bigchem/online-chem.

Predicting drug activity against cancer cells by random forest models based on minimal genomic information and chemical properties.

A key goal of precision medicine is predicting the best drug therapy for a specific patient from genomic information. In oncology, cancers that appear similar pathologically can vary greatly in how they respond to the same drug. Fortunately, data from high-throughput screening programs often reveal important relationships between genomic variability of cancer cells and their response to drugs. Nevertheless, many current computational methods to predict compound activity against cancer cells require large quantities of genomic, epigenomic, and additional cellular data to develop and to apply. Here we integrate recent screening data and machine learning to train classification models that predict the activity/inactivity of compounds against cancer cells based on the mutational status of only 145 oncogenes and a set of compound structural descriptors. Using IC50 values of 1 µM as activity cutoffs, our predictive models have sensitivities of 87%, specificities of 87%, and yield an area under the receiver operating characteristic curve equal to 0.94. We also develop regression models to predict log(IC50) values of compounds for cancer cells; the models achieve a Pearson correlation coefficient of 0.86 for cross-validation and up to 0.65-0.73 against blind test sets. Predictive performance remains strong when as few as 50 oncogenes are included. Finally, even when 40% of experimental IC50 values are missing from screening data, they can be imputed with sufficient reliability that classification accuracy is not diminished. The presented models are fast to generate and may serve as easily implemented screening tools for personalized oncology medicine, drug repurposing, and drug discovery.

Metatox - Web application for generation of metabolic pathways and toxicity estimation.

Xenobiotics biotransformation in humans is a process of the chemical modifications, which may lead to the formation of toxic metabolites. The prediction of such metabolites is very important for drug development and ecotoxicology studies. We created the web-application MetaTox ( http://way2drug.com/mg ) for the generation of xenobiotics metabolic pathways in the human organism. For each generated metabolite, the estimations of the acute toxicity (based on GUSAR software prediction), organ-specific carcinogenicity and adverse effects (based on PASS software prediction) are performed. Generation of metabolites by MetaTox is based on the fragments datasets, which describe transformations of substrates structures to a metabolites structure. We added three new classes of biotransformation reactions: Dehydrogenation, Glutathionation, and Hydrolysis, and now metabolite generation for 15 most frequent classes of xenobiotic's biotransformation reactions are available. MetaTox calculates the probability of formation of generated metabolite - it is the integrated assessment of the biotransformation reactions probabilities and their sites using the algorithm of PASS ( http://way2drug.com/passonline ). The prediction accuracy estimated by the leave-one-out cross-validation (LOO-CV) procedure calculated separately for the probabilities of biotransformation reactions and their sites is about 0.9 on the average for all reactions.

Molecular property diagnostic suite for diabetes mellitus (MPDSDM): An integrated web portal for drug discovery and drug repurposing.

Molecular Property Diagnostic Suite - Diabetes Mellitus (MPDSDM) is a Galaxy-based, open source disease-specific web portal for diabetes. It consists of three modules namely (i) data library (ii) data processing and (iii) data analysis tools. The data library (target library and literature) module provide extensive and curated information about the genes involved in type 1 and type 2 diabetes onset and progression stage (available at http://www.mpds-diabetes.in). The database also contains information on drug targets, biomarkers, therapeutics and associated genes specific to type 1, and type 2 diabetes. A unique MPDS identification number has been assigned for each gene involved in diabetes mellitus and the corresponding card contains chromosomal data, gene information, protein UniProt ID, functional domains, druggability and related pathway information. One of the objectives of the web portal is to have an open source data repository that contains all information on diabetes and use this information for developing therapeutics to cure diabetes. We also make an attempt for computational drug repurposing for the validated diabetes targets. We performed virtual screening of 1455 FDA approved drugs on selected 20 type 1 and type 2 diabetes proteins using docking protocol and their biological activity was predicted using "PASS Online" server (http://www.way2drug.com/passonline) towards anti-diabetic activity, resulted in the identification of 41 drug molecules. Five drug molecules (which are earlier known for anti-malarial/microbial, anti-viral, anti-cancer, anti-pulmonary activities) were proposed to have a better repurposing potential for type 2 anti-diabetic activity and good binding affinity towards type 2 diabetes target proteins.

Dynamic Effects of Two-Time Delays on a Model for Tumor Growth.

AIMS AND OBJECTIVE: In order to understand the dynamic mechanisms of tumor growth and make a contribution to develop anti-cancer treatment strategies, a mathematical model for tumor growth with two-time delays is proposed in this article. MATERIALS AND METHODS: First, the relationships among host cells, tumor cells and effector cells, and the biological meaning of two-time delays are explained. Moreover, the system stability is discussed by analyzing the characteristic equation of the model. In addition, the existence and properties of oscillatory dynamic are also researched by using normative theory and central manifold method. Finally, the numerical simulations are performed to further illustrate and support the theoretical results. RESULTS: Both two-time delays in the model can affect the dynamics of tumor growth. Meanwhile, the system can experience a Hopf bifurcation when the delay crosses a series of critical values. Further, a clear formula is deduced to determine the Hopf bifurcation and the direction of stability of the periodic solution. Finally, these results are verified by using numerical simulation. CONCLUSION: The results demonstrated that the time from identifying tumor cells to making the appropriate response for the immune system and the time needed for competition between host cells and tumor cells for natural resources and living space is significant for tumor growth. These findings in this paper may help us better understand the behaviors of tumors and develop better anti-cancer treatment strategies.

eRepo-ORP: Exploring the Opportunity Space to Combat Orphan Diseases with Existing Drugs.

About 7000 rare, or orphan, diseases affect more than 350 million people worldwide. Although these conditions collectively pose significant health care problems, drug companies seldom develop drugs for orphan diseases due to extremely limited individual markets. Consequently, developing new treatments for often life-threatening orphan diseases is primarily contingent on financial incentives from governments, special research grants, and private philanthropy. Computer-aided drug repositioning is a cheaper and faster alternative to traditional drug discovery offering a promising venue for orphan drug research. Here, we present eRepo-ORP, a comprehensive resource constructed by a large-scale repositioning of existing drugs to orphan diseases with a collection of structural bioinformatics tools, including eThread, eFindSite, and eMatchSite. Specifically, a systematic exploration of 320,856 possible links between known drugs in DrugBank and orphan proteins obtained from Orphanet reveals as many as 18,145 candidates for repurposing. In order to illustrate how potential therapeutics for rare diseases can be identified with eRepo-ORP, we discuss the repositioning of a kinase inhibitor for Ras-associated autoimmune leukoproliferative disease. The eRepo-ORP data set is available through the Open Science Framework at https://osf.io/qdjup/.

CATTLE (CAncer treatment treasury with linked evidence): An integrated knowledge base for personalized oncology research and practice.

Despite the existence of various databases cataloging cancer drugs, there is an emerging need to support the development and application of personalized therapies, where an integrated understanding of the clinical factors and drug mechanism of action and its gene targets is necessary. We have developed CATTLE (CAncer Treatment Treasury with Linked Evidence), a comprehensive cancer drug knowledge base providing information across the complete spectrum of the drug life cycle. The CATTLE system collects relevant data from 22 heterogeneous databases, integrates them into a unified model centralized on drugs, and presents comprehensive drug information via an interactive web portal with a download function. A total of 2,323 unique cancer drugs are currently linked to rich information from these databases in CATTLE. Through two use cases, we demonstrate that CATTLE can be used in supporting both research and practice in personalized oncology.

INFERENCE OF PERSONALIZED DRUG TARGETS VIA NETWORK PROPAGATION.

We present a computational strategy to simulate drug treatment in a personalized setting. The method is based on integrating patient mutation and differential expression data with a protein-protein interaction network. We test the impact of in-silico deletions of different proteins on the flow of information in the network and use the results to infer potential drug targets. We apply our method to AML data from TCGA and validate the predicted drug targets using known targets. To benchmark our patient-specific approach, we compare the personalized setting predictions to those of the conventional setting. Our predicted drug targets are highly enriched with known targets from DrugBank and COSMIC (p < 10(-5) outperforming the non-personalized predictions. Finally, we focus on the largest AML patient subgroup (~30%) which is characterized by an FLT3 mutation, and utilize our prediction score to rank patient sensitivity to inhibition of each predicted target, reproducing previous findings of in-vitro experiments.

Utility-based optimization of phase II/III programs.

Phase II and phase III trials play a crucial role in drug development programs. They are costly and time consuming and, because of high failure rates in late development stages, at the same time risky investments. Commonly, sample size calculation of phase III is based on the treatment effect observed in phase II. Therefore, planning of phases II and III can be linked. The performance of the phase II/III program crucially depends on the allocation of the resources to phases II and III by appropriate choice of the sample size and the rule applied to decide whether to stop the program after phase II or to proceed. We present methods for a program-wise phase II/III planning that aim at determining optimal phase II sample sizes and go/no-go decisions in a time-to-event setting. Optimization is based on a utility function that takes into account (fixed and variable) costs of the drug development program and potential gains after successful launch. The proposed methods are illustrated by application to a variety of scenarios typically met in oncology drug development.

Windows of opportunities and technological innovation in the Brazilian pharmaceutical industry / Ventanas de oportunidades e innovación tecnológica en la industria brasileña de medicamentos / Janelas de oportunidades e inovação tecnológica na indústria brasileira de medicamentos

Abstract: The Brazilian pharmaceutical industry is heavily dependent on external sources of inputs, capital, and technology. However, the emergence of technological opportunities and the development of biotechnology and the decline of the patent boom and resulting advances by generic drugs have opened windows of opportunities for the local industry. The article examines the Brazilian industry's innovative behavior vis-à-vis these opportunities, showing that although the industry as a whole invests little in innovation, a few large Brazilian companies have expanded their market share and stepped up their investments in research and development, supported by public policies for innovation.

Resumen: La industria farmacéutica brasileña se caracteriza por su gran dependencia de fuentes externas de insumos, capital y tecnología. El surgimiento de oportunidades tecnológicas, asociadas al desarrollo de la biotecnología, y al fin del boom de las patentes -con el consecuente avance de los medicamentos genéricos-, es paralelo a la apertura de ventanas de oportunidad para la industria local. Este artículo examina el comportamiento innovador de la industria brasileña a la luz de esas oportunidades, revelando que, aunque el conjunto de la industria mantenga bajos niveles de inversión en innovación, un pequeño grupo de grandes empresas nacionales está ampliando su participación en el mercado e intensificando sus inversiones en pesquisa y desarollo, apoyados por políticas públicas de innovación.

Resumo: A indústria farmacêutica brasileira caracteriza-se pela grande dependência de fontes externas de insumos, capital e tecnologia. O surgimento de oportunidades tecnológicas, associadas ao desenvolvimento da biotecnologia e ao fim do boom das patentes com o consequente avanço dos medicamentos genéricos, entretanto, vem abrindo janelas de oportunidades para a indústria local. Este artigo examina o comportamento inovador da indústria brasileira à luz dessas oportunidades, revelando que, embora o conjunto da indústria mantenha baixos níveis de investimentos em inovação, um pequeno grupo de grandes empresas nacionais vem ampliando sua participação no mercado e intensificando seus investimentos em pesquisa e desenvolvimento, apoiados por políticas públicas de inovação.

Discovery and development of antiplasmodial compounds in Thailand during the 21st century.

This review describes research conducted in Thailand from 2000 to 2013 on the discovery of new compounds from local flora and fauna, including those of marine organisms from coastal regions, which have antiplasmodial activity against Plasmodium falciparum growth in culture. These antiplasmodials comprised alkaloids, angucyclinones, anthraquinones, azaanthraquinone, azaphilones, ben- zoquinones, bioxanthracenes, carbazomycins, chalcones, chromone, clerodane, coumarins, cyclomarin, cyclopeptides, cytochalasins, depsidones, depudecin, flavaglines, flavonoids, furans, isoflavonoid limonoids, macrolides, nucleoside, oxepin, peptides, phloroglucinol, polylactone, polypropionate, preussomerins, prodigiosin, pterocarpans, pyrenocines, pyridones, pyrrolidines, quassinoids, quinone, stilbenes, styryl lactones, terpenoids, tetramic acids, tetronic acids, tri- norcadalenes, tropolones, xanthones, and a variety of miscellaneous molecules (a total of 293 compounds). The review also describes the screening and synthesis of novel chemicals targeted against parasite enzymes, (carbonic anhydrase, cy- tochrome bcl, dihydrofolate reductase and orotidine 5'-monophosphate decar- boxylase), which have the potential of being developed into antimalarial drugs. Possible future trends in antimalarial drug research in Thailand are discussed. This review describes research conducted in Thailand from 2000 to 2013

Screening for noise in gene expression identifies drug synergies.

Stochastic fluctuations are inherent to gene expression and can drive cell-fate specification. We used such fluctuations to modulate reactivation of HIV from latency-a quiescent state that is a major barrier to an HIV cure. By screening a diverse library of bioactive small molecules, we identified more than 80 compounds that modulated HIV gene-expression fluctuations (i.e., "noise"), without changing mean expression. These noise-modulating compounds would be neglected in conventional screens, and yet, they synergized with conventional transcriptional activators. Noise enhancers reactivated latent cells significantly better than existing best-in-class reactivation drug combinations (and with reduced off-target cytotoxicity), whereas noise suppressors stabilized latency. Noise-modulating chemicals may provide novel probes for the physiological consequences of noise and an unexplored axis for drug discovery, allowing enhanced control over diverse cell-fate decisions.

De novo design of drug-like molecules by a fragment-based molecular evolutionary approach.

This paper describes a similarity-driven simple evolutionary approach to producing candidate molecules of new drugs. The aim of the method is to explore the candidates that are structurally similar to the reference molecule and yet somewhat different in not only peripheral chains but also their scaffolds. The method employs a known active molecule of our interest as a reference molecule which is used to navigate a huge chemical space. The reference molecule is also used to obtain seed fragments. An initial set of individual structures is prepared with the seed fragments and additional fragments using several connection rules. The fragment library is preferably prepared from a collection of known molecules related to the target of the reference molecule. Every fragment of the library can be used for fragment-based mutation. All the fragments are categorized into three classes; rings, linkers, and side chains. New individuals are produced by the crossover and the fragment-based mutation with the fragment library. Computer experiments with our own fragment library prepared from GPCR SARfari verified the feasibility of our approach to drug discovery.