ArticleLiterature Review

Applying computational modeling to drug discovery and development

Authors:
  • Comet Therapeutics
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Abstract

Computational models of cells, tissues and organisms are necessary for increased understanding of biological systems. In particular, modeling approaches will be crucial for moving biology from a descriptive to a predictive science. Pharmaceutical companies identify molecular interventions that they predict will lead to therapies at the organism level, suggesting that computational biology can play a key role in the pharmaceutical industry. We discuss pharmaceutically-relevant computational modeling approaches currently used as predictive tools. Specific examples demonstrate how companies can employ these computational models to improve the efficiency of transforming targets into therapies.

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... Applying systems biology in drug discovery requires integrating the processes of systems biology studies with the processes -the systems -governing research and development in a large organization. Dynamic systems models of biology also have a role in drug discovery (Fitzgerald, Schoeberl et al. 2006;Kumar, Hendriks et al. 2006), and may play an essential bridging function with the increasing importance of quantitative pharmacology and model-based approaches to drug development (Zhang, Sinha et al. 2006;Zhang, Pfister et al. 2008). ...
... In discovering and developing novel treatments, the challenge is not only to identify new targets, but to leverage the growing wealth of integrative clinical genomic data to drive clinically relevant decisions before clinical trials begin. Data driven models and dynamic multi-scale models of biological systems -intracellular, multicellular, and physiological -can together leverage this data to provide more accurate and useful model-based tools for representing biology (Kumar, Hendriks et al. 2006), transforming drug discovery to a more holistic, model based approach that accelerates innovation and improves outcome in individual patients. ...
Chapter
This chapter discusses systems-based approaches to translational medicine. This is made possible by the convergent acceleration of several disciplines—computational, post-genomic platform and biomedical sciences—working together to offer a more complete picture of the complexities of disease and disease treatment. Systems-based approaches offer an increasingly less biased set of platforms with which to observe the phenomenal diversity of cancers and conduct experiments in a wider range of more clinically relevant models. This chapter also describes how systems approaches for predictive biomedicine—integrating discovery and clinical data—can be applied to identify novel targets, predictive biomarkers of response to agents in development and targeted use of drug combinations. The objective of these approaches is for new agents, the development of which is guided by predictive biomedicine to improve outcomes for individual cancer patients. Some more recently approved agents, such as sorafenib, intentionally target several members of signaling cascades dysregulated in tumors, taking advantage of the multitargeted nature of many kinase inhibitors. There has also been a phenomenal explosion in the generation of experimental data in the wake of the human genome project. Rapid advances in computational processing power, data storage, modeling, and analysis have become available to make such data interpretable. The convergence of these three trends—targeted therapies in cancer, post-genomic system-wide experimentation platforms, and unprecedented advances in information technology—lays the foundation for the application of systems biology to drug discovery in cancer.
... Computational chemistry is an ever growing field with the combination of computational power, chemical and biological space that streamlines drug discovery, development and organization. Structure based drug design and ligand based drug design are the two most important and commonly used computational approaches (Kumar et al., 2006). Even though structure based drug design holds a few drawbacks, it assists the Downloaded by [134.117. ...
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Chronic Kidney Disease (CKD) is a prominent health issue reported globally. The level of the vitamin D receptor (VDR) and cytochrome P450 enzyme 24-hydroxylase (CYP24A1) are crucial in the pathogenesis of secondary hyperparathyroidism (sHPT) in CKD. An elevated expression of the CYP24A1 leads to the deficiency of vitamin D and resistance to vitamin D therapy. Hence, VDR agonists and CYP24A1 antagonists are suggested to CKD patients for the management of biochemical complications. CTA-018 is a recently reported analogue and acts as a potent CYP24A1 inhibitor. It inhibits CYP24A1 with an IC50 27 ± 6 nM, about 10 times more potentially than the non-selective inhibitor ketoconazole (253 ± 20 nM), and it is also been reported to induce the VDR expression. Thus, CTA-018 is under clinical trial among CKD patients. In this study, combined molecular docking and pharmacophore filtering were employed to identify compounds better than CTA-018. A huge set of 9127 compounds from Sweet Lead database were docked into the active site of VDR using Glide XP program. E-Pharmacophore was developed from both the targets along with CTA-018. The compounds retrieved from the two different pharmacophore based screening were re-docked into the active site of CYP24A1. The hits that bind well at both the active sites and matched with the pharmacophore models were considered as possible dual functional molecules against VDR and CYP24A1. Further, molecular dynamics simulation and subsequent energy decomposition analyses were also performed to study the role of specific amino acids in the active site of both VDR and CYP24A1.
... Moreover, various programs like iVAX, Vaxign, NERVE, and PEPVAC are available for vaccine research and development [9][10][11][12] . Due to efficacy, accuracy, and cost-effectiveness of computational vaccinology, nowadays numerous pharmaceutical companies and academic centers are using this strategy for vaccine discovery programs 13 . ...
Article
Helicobacter pylori is the cunning bacterium that can live in the stomachs of many people without any symptoms, but gradually can lead to gastric cancer. Due to various obstacles, which are related to anti-H. pylori antibiotic therapy, recently developing an anti-H. pylori vaccine has attracted more attention. In this study, different immunoinformatics and computational vaccinology approaches were employed to design an efficient multi-epitope oral vaccine against H. pylori. Our multi-epitope vaccine is composed of heat labile enterotoxin IIc B (LT-IIc) that is used as a mucosal adjuvant to enhance vaccine immunogenicity for oral immunization, cartilage oligomeric matrix protein (COMP) to increase vaccine stability in acidic pH of gut, one experimentally protective antigen, OipA, and two hypothetical protective antigens, HP0487 and HP0906, and "CTGKSC" peptide motif that target epithelial microfold cells (M cells) to enhance vaccine uptake from the gut barrier. All the aforesaid segments were joined to each other by proper linkers. The vaccine construct was modeled, validated, and refined by different programs to achieve a high-quality 3D structure. The resulting high-quality model was applied for conformational B-cell epitopes selection and docking analyses with a toll-like receptor 2 (TLR2). Moreover, molecular dynamics studies demonstrated that the protein-TLR2 docked model was stable during simulation time. We believe that our vaccine candidate can induce mucosal sIgA and IgG antibodies, and Th1/Th2/Th17-mediated protective immunity that are crucial for eradicating H. pylori infection. In sum, the computational results suggest that our newly designed vaccine could serve as a promising anti-H. pylori vaccine candidate.
... Therefore, computational models have the potential to transform experimental biology by describing and understanding observations and ultimately to predict cell behavior and to assist with the design of new biological experiments. The outcomes of biological experiments will either validate the model or will identify novel mechanisms that can be incorporated in the model, and thus computational modeling enhances the accuracy and predictive potential of biological concepts (Kumar et al., 2006). ...
Article
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A fundamental question in cartilage biology is: what determines the switch between permanent cartilage found in the articular joints and transient hypertrophic cartilage that functions as a template for bone? This switch is observed both in a subset of OA patients that develop osteophytes, as well as in cell-based tissue engineering strategies for joint repair. A thorough understanding of the mechanisms regulating cell fate provides opportunities for treatment of cartilage disease and tissue engineering strategies. The objective of this study was to understand the mechanisms that regulate the switch between permanent and transient cartilage using a computational model of chondrocytes, ECHO. To investigate large signaling networks that regulate cell fate decisions, we developed the software tool ANIMO, Analysis of Networks with interactive Modeling. In ANIMO, we generated an activity network integrating 7 signal transduction pathways resulting in a network containing over 50 proteins with 200 interactions. We called this model ECHO, for executable chondrocyte. Previously, we showed that ECHO could be used to characterize mechanisms of cell fate decisions. ECHO was first developed based on a Boolean model of growth plate. Here, we show how the growth plate Boolean model was translated to ANIMO and how we adapted the topology and parameters to generate an articular cartilage model. In ANIMO, many combinations of overactivation/knockout were tested that result in a switch between permanent cartilage (SOX9+) and transient, hypertrophic cartilage (RUNX2+). We used model checking to prioritize combination treatments for wet-lab validation. Three combinatorial treatments were chosen and tested on metatarsals from 1-day old rat pups that were treated for 6 days. We found that a combination of IGF1 with inhibition of ERK1/2 had a positive effect on cartilage formation and growth, whereas activation of DLX5 combined with inhibition of PKA had a negative effect on cartilage formation and growth and resulted in increased cartilage hypertrophy. We show that our model describes cartilage formation, and that model checking can aid in choosing and prioritizing combinatorial treatments that interfere with normal cartilage development. Here we show that combinatorial treatments induce changes in the zonal distribution of cartilage, indication possible switches in cell fate. This indicates that simulations in ECHO aid in describing pathologies in which switches between cell fates are observed, such as OA.
... Furthermore, a new quantitative systems pharmacology (QSP) discipline has recently emerged, that uses mathematical computational models to describe biological systems, disease progression and drug pharmacology in a single modelling framework [327][328][329]. QSP combines elements of translational drug pharmacokinetics (PK), pharmacodynamics (PD) and systems biology to address clinical needs, such as representation of patient variability for dose selection, demonstration of long-term disease progression to find optimal treatment, and facilitation of modelling accessibility for clinicians and pharmaceutical companies in order to understand the diseases [330]. ...
Article
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Cardiovascular disease is the leading cause of death worldwide. Although investment in drug discovery and development has been sky-rocketing, the number of approved drugs has been declining. Cardiovascular toxicity due to therapeutic drug use claims the highest incidence and severity of adverse drug reactions in late-stage clinical development. Therefore, to address this issue, new, additional, replacement and combinatorial approaches are needed to fill the gap in effective drug discovery and screening. The motivation for developing accurate, predictive models is twofold: first, to study and discover new treatments for cardiac pathologies which are leading in worldwide morbidity and mortality rates; and second, to screen for adverse drug reactions on the heart, a primary risk in drug development. In addition to in vivo animal models, in vitro and in silico models have been recently proposed to mimic the physiological conditions of heart and vasculature. Here, we describe current in vitro, in vivo, and in silico platforms for modelling healthy and pathological cardiac tissues and their advantages and disadvantages for drug screening and discovery applications. We review the pathophysiology and the underlying pathways of different cardiac diseases, as well as the new tools being developed to facilitate their study. We finally suggest a roadmap for employing these non-animal platforms in assessing drug cardiotoxicity and safety.
... For effective drug discovery and intervention, it is critical to have an understanding of the complex mapping between genotype and phenotype, an evaluation of the regulatory interaction among genes, proteins, and other molecules and the effect of perturbations and other biological processes at the molecular, cellular, and tissue/organ scales. 8,[11][12][13] Thus, it is imperative to have models that can predict and provide functional insights into disease-drug interactions and pharmacokinetics/pharmacodynamics (PK/PD) information, to ensure that therapeutic intervention becomes a more systematic and faster process. A major challenge is linking drug PK characteristics with PD information for a better grasp of the time course of drug effects after drug intake. ...
Article
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Effective cancer treatment strategy requires an understanding of cancer behavior and development across multiple temporal and spatial scales. This has resulted into a growing interest in developing multiscale mathematical models that can simulate cancer growth, development, and response to drug treatments. This study thus investigates multiscale tumor modeling that integrates drug pharmacokinetic and pharmacodynamic (PK/PD) information using stochastic hybrid system modeling framework. Specifically, (1) pathways modeled by differential equations are adopted for gene regulations at the molecular level; (2) cellular automata (CA) model is proposed for the cellular and multicellular scales. Markov chains are used to model the cell behaviors by taking into account the gene expression levels, cell cycle, and the microenvironment. The proposed model enables the prediction of tumor growth under given molecular properties, microenvironment conditions, and drug PK/PD profile. Simulation results demonstrate the effectiveness of the proposed approach and the results agree with observed tumor behaviors.
... [11] All these techniques are becoming very important part of drug discovery and development process and it is expected that the computational method can help in improving the efficiency for the industry by decreasing the requirement of resources. [12] Molecular modeling includes all the methods either theoretical or computational used for biology, drug design, materials science and computational chemistry for the study of molecular systems which ranges from small chemical systems to large biological molecules and assemblies of material. Manual approach can be used for simple calculations, but for carrying out the molecular modeling of larger sized system computers are needed. ...
Article
Full-text available
Various drug discovery programs have employed molecular modeling methods in pharmaceutical research for studying the complex chemical and biological systems. Experimental and computational strategies when used in combination, plays a very important role in the development and identification of novel compounds. The method of molecular docking is being broadly utilized in modern drug design, for exploring the confirmation of ligand within the target's binding sites. With molecular docking the binding energy of the ligand with receptor can also be estimated. For this purpose, various docking algorithms are available nowadays and it is very important to understand the limitations and advantages of each of these methods for developing efficient strategies and for generating appropriate results. In this review the basics of computational drug designing process has been discussed along with introduction molecular docking method and its application in drug discovery and medicinal chemistry.
... In addition, to effectively fight against costly termination of drugs in the clinical phase, the pharmaceutical industry has been keen to invest in theoretical and computational modeling to promote the drug discovery process (23)(24)(25)(26)(27), which enabled a recursive process through hypothesis testing and bench experimentation (28,29). Models are fast to execute, able to reduce the use of animals and offer cheap predictive solutions for drug pharmacokinetics (PK), pharmacodynamics (PD) as well as patient population responses. ...
Chapter
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This review focuses on how system biology may assist techniques that are used in pharmacological research, such as high-throughput screening, high-throughput analytical characterization of biological samples, preclinical and clinical trials, as well as targets and drug validation in order to reach patients at the lowest possible cost in a translational perspective. In signaling networks, targets can be assessed through topological criteria such as their connectivity and/or centrality. In metabolic networks, the relevance of a target for drug development may rather be assessed through some sort of enzymatic specificity resulting from remote homology, analogy, or specificity in its strict sense. The concept of specificity is especially valuable in the context of a host-parasite relationship where targeting a protein specific of a parasite compared to its host is expected to minimize the noxious collateral effects of the inhibitor to the host. The relevance of putative molecular target must be proven through bench and animal validations prior to going through clinical trials. Flux balance analysis and other modeling methods of system biology enable to assess whether a molecular target can be considered as pathway’s choke or not in a network context, which may facilitate the decision of developing drugs for it.
... For example, epitope-mapping algorithms have been used for vaccine design since the 1980s [35]. Since then, new computational tools have been used for selection of vaccine targets [36][37][38][39][40][41][42][43][44]. Most of the works focuses on using mathematical and computational tools to predict epitopes [45] or to develop virtual screening approaches (i.e, the identification of relevant antigens) [46][47][48][49]. ...
Article
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Background Although a safe and effective yellow fever vaccine was developed more than 80 years ago, several issues regarding its use remain unclear. For example, what is the minimum dose that can provide immunity against the disease? A useful tool that can help researchers answer this and other related questions is a computational simulator that implements a mathematical model describing the human immune response to vaccination against yellow fever. Methods This work uses a system of ten ordinary differential equations to represent a few important populations in the response process generated by the body after vaccination. The main populations include viruses, APCs, CD8+ T cells, short-lived and long-lived plasma cells, B cells and antibodies. Results In order to qualitatively validate our model, four experiments were carried out, and their computational results were compared to experimental data obtained from the literature. The four experiments were: a) simulation of a scenario in which an individual was vaccinated against yellow fever for the first time; b) simulation of a booster dose ten years after the first dose; c) simulation of the immune response to the yellow fever vaccine in individuals with different levels of naïve CD8+ T cells; and d) simulation of the immune response to distinct doses of the yellow fever vaccine. Conclusions This work shows that the simulator was able to qualitatively reproduce some of the experimental results reported in the literature, such as the amount of antibodies and viremia throughout time, as well as to reproduce other behaviors of the immune response reported in the literature, such as those that occur after a booster dose of the vaccine.
... [11] All these techniques are becoming very important part of drug discovery and development process and it is expected that the computational method can help in improving the efficiency for the industry by decreasing the requirement of resources. [12] Molecular modeling includes all the methods either theoretical or computational used for biology, drug design, materials science and computational chemistry for the study of molecular systems which ranges from small chemical systems to large biological molecules and assemblies of material. Manual approach can be used for simple calculations, but for carrying out the molecular modeling of larger sized system computers are needed. ...
... There are also many other functional phenotype-based approaches that use the CMap resource to understand MoA [7,[78][79][80]. It is widely known that many drugs with therapeutic targets in cancer prognosis and diagnosis have been identified using CMap. ...
Article
Full-text available
Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.
... In general, recognizing that drug discovery and development are a time and resource consuming process, various stages of screening are required. There is an ever-growing attempt to apply computational power to the combined chemical and biological space in order to streamline drug discovery, design, development and optimization [45]. An efficient way to depict the substrate/inhibitor/activator binding modes with the active site/binding site residues of the receptor (docking) have been successfully implemented in many applications [46,47]. ...
Article
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Bacterial meningitis, an infection of the membranes (meninges) and cerebrospinal fluid (CSF) surrounding the brain and spinal cord, is a major cause of death and disability worldwide. Streptococcus pneumonia, Neisseria meningitidis, Haemophilus influenzae type b and Staphylococcus aureus are the predominant pathogens of bacterial meningitis. In our previous study, we have identified lumazine synthase as a common drug target in pathogens of bacterial meningitis. The three dimensional (3D) structure of lumazine synthase complex with 5-nitro-6-ribityl-amino-2,4(1h, 3h)-pyrimidinedione (INI) was generated based on PDBID: 1RVV as a template using Modeller 9v12. Multiple docking strategies including rigid receptor docking (RRD), QPLD and IFD were performed for lumazine synthase with 29 existing inhibitors, and screened for 13050 ligands, respectively. Three stages of RRD (HTVS, SP, XP) were carried out using Glide v5.9 resulted in 134 leads. The 134 leads were compared to 29 inhibitors; eight best leads were obtained, which were further utilized for QPLD-MM-GBSA analysis. Eight compounds had a least docking score, lower binding free energy, and better binding orientation towards lumazine synthase. Furthermore, to analyse the stability of the lumazine synthase, the lead1 complex was subjected to 50 ns MD simulations and found to be stable. The results from multiple docking analysis and MD simulations affirmed that lead1 may be a promising inhibitor for efficient inhibition of lumazine synthase activity in common pathogens of bacterial meningitis.
... Systems biology approaches can provide a solution to design lifesaving and cost-effective drugs so that the diseases can be cured and prevented. Systems based computational techniques will be highly useful in designing effective therapeutic drugs (6,7). System biology is defining biochemical networks in which biomolecules are showing the nodes and the molecular interactions between them are called the edges (8,9). ...
... Since then, new computational tools have been used for selection of vaccine targets, see. [26][27][28][29][30][31][32][33][34] Pappalardo et al. 35 present a survey of several computational modeling techniques applied to vaccinology science. Most of the works focus on using computational science to predict epitopes, as the work of Lafuente and Reche, 36 or to develop virtual screening approaches, see. ...
Article
Full-text available
New contributions that aim to accelerate the development or to improve the efficacy and safety of vaccines arise from many different areas of research and technology. One of these areas is computational science, which traditionally participates in the initial steps, such as the pre-screening of active substances that have the potential to become a vaccine antigen. In this work, we present another promising way to use computational science in vaccinology: mathematical and computational models of important cell and protein dynamics of the immune system. A system of Ordinary Differential Equations represents different immune system populations, such as B cells and T cells, antigen presenting cells and antibodies. In this way, it is possible to simulate, in silico, the immune response to vaccines under development or under study. Distinct scenarios can be simulated by varying parameters of the mathematical model. As a proof of concept, we developed a model of the immune response to vaccination against the yellow fever. Our simulations have shown consistent results when compared with experimental data available in the literature. The model is generic enough to represent the action of other diseases or vaccines in the human immune system, such as dengue and Zika virus.
... Models offer cheap predictive solutions for drug pharmacokinetics (PK), pharmacodynamics (PD) and patient population responses. Models are also capable of providing novel insights into fundamental biology which furthers our understanding of nature and diseases [19,20]. ...
Article
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Recent growth in annual new therapeutic entity (NTE) approvals by the U.S. Food and Drug Administration (FDA) suggests a positive trend in current research and development (R&D) output. Prior to this, the cost of each NTE was considered to be rising exponentially, with compound failure occurring mainly in clinical phases. Quantitative systems pharmacology (QSP) modelling, as an additional tool in the drug discovery arsenal, aims to further reduce NTE costs and improve drug development success. Through in silico mathematical modelling, QSP can simulate drug activity as perturbations in biological systems and thus understand the fundamental interactions which drive disease pathology, compound pharmacology and patient response. Here we review QSP, pharmacometrics and systems biology models with respect to the diseases covered as well as their clinical relevance and applications. Overall, the majority of modelling focus was aligned with the priority of drug-discovery and clinical trials. However, a few clinically important disease categories, such as Immune System Diseases and Respiratory Tract Diseases, were poorly covered by computational models. This suggests a possible disconnect between clinical and modelling agendas. As a standard element of the drug discovery pipeline the uptake of QSP might help to increase the efficiency of drug development across all therapeutic indications.
... It has also been discussed that molecules that could target epigenetic modifiers could be a potential new avenue for drug development [41]. In fact, targeting epigenetic modifiers as potential drug targets have been extensively discussed and pursued [42,43]. The cornerstone of any rational drug discovery process starts from systematic screening of molecular libraries against target proteins, and assaying them for their biological outputs or phenotypes. ...
Article
Full-text available
Background: The dynamic and differential regulation and expression of genes is majorly governed by the complex interactions of a subset of biomolecules in the cell operating at multiple levels starting from genome organisation to protein post-translational regulation. The regulatory layer contributed by the epigenetic layer has been one of the favourite areas of interest recently. This layer of regulation as we know today largely comprises of DNA modifications, histone modifications and noncoding RNA regulation and the interplay between each of these major components. Epigenetic regulation has been recently shown to be central to development of a number of disease processes. The availability of datasets of high-throughput screens for molecules for biological properties offer a new opportunity to develop computational methodologies which would enable in-silico screening of large molecular libraries. Methods: In the present study, we have used data from high throughput screens for the inhibitors of epigenetic modifiers. Computational predictive models were constructed based on the molecular descriptors. Machine learning algorithms for supervised training, Naive Bayes and Random Forest, were used to generate predictive models for the small molecule inhibitors of histone methyl-transferases and demethylases. Random forest, with the accuracy of 80%, was identified as the most accurate classifier. Further we complemented the study with substructure search approach filtering out the probable pharmacophores from the active molecules leading to drug molecules. Results: We show that effective use of appropriate computational algorithms could be used to learn molecular and structural correlates of biological activities of small molecules. The computational models developed could be potentially used to screen and identify potential new biological activities of molecules from large molecular libraries and prioritise them for in-depth biological assays. To the best of our knowledge, this is the first and most comprehensive computational analysis towards understanding activities of small molecules inhibitors of epigenetic modifiers.
... A spectrum of frameworks exists for the modeling of cell signaling networks, each with its own strengths and weaknesses. [11][12][13] We recently established a formalism for modeling quantitative logic relationships called constrained fuzzy logic (CFL), which allows for the modeling of larger networks than would be feasible using differential equations while providing enhanced insights compared to simpler Boolean logic models. 14 We have also developed software named Querying Quantitative Logic Models (Q2LM) to efficiently simulate CFL models in response to multiple microenvironments and drug treatments. ...
Article
Full-text available
A major challenge in developing anticancer therapies is determining the efficacies of drugs and their combinations in physiologically relevant microenvironments. We describe here our application of “constrained fuzzy logic” (CFL) ensemble modeling of the intracellular signaling network for predicting inhibitor treatments that reduce the phospho-levels of key transcription factors downstream of growth factors and inflammatory cytokines representative of hepatocellular carcinoma (HCC) microenvironments. We observed that the CFL models successfully predicted the effects of several kinase inhibitor combinations. Furthermore, the ensemble predictions revealed ambiguous predictions that could be traced to a specific structural feature of these models, which we resolved with dedicated experiments, finding that IL-1α activates downstream signals through TAK1 and not MEKK1 in HepG2 cells. We conclude that CFL-Q2LM (Querying Quantitative Logic Models) is a promising approach for predicting effective anticancer drug combinations in cancer-relevant microenvironments.
... The classical design of a new drug usually requires a series of lengthy steps that follow the typical pharmaceutical research and development (R&D) pipeline [1]. Nowadays a multitude of computational applications can be used to speed-up the R&D pipeline in the development of new treatments. ...
Article
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.
... For example, to understand how an insulin secretagogue will affect plasma glucose concentrations, researchers must consider its PK properties, its concentration-dependent effect on insulin secretion and possibly other pathways, the role of insulin in regulating glucose, and feedbacks that could amplify or dampen response. [24][25][26] Mechanistic physiological models, such as the example of a type 2 Diabetes PhysioPD Research Platform developed by Rosa & Co (Figure 1), have been used to integrate relevant existing information in order to address these types of questions explicitly. ...
Article
Full-text available
Mechanistic physiological modeling is a scientific method that combines available data with scientific knowledge and engineering approaches to facilitate better understanding of biological systems, improve decision-making, reduce risk, and increase efficiency in drug discovery and development. It is a type of quantitative systems pharmacology (QSP) approach that places drug-specific properties in the context of disease biology. This tutorial provides a broadly applicable model qualification method (MQM) to ensure that mechanistic physiological models are fit for their intended purposes.
... [11] All these techniques are becoming very important part of drug discovery and development process and it is expected that the computational method can help in improving the efficiency for the industry by decreasing the requirement of resources. [12] Molecular modeling includes all the methods either theoretical or computational used for biology, drug design, materials science and computational chemistry for the study of molecular systems which ranges from small chemical systems to large biological molecules and assemblies of material. Manual approach can be used for simple calculations, but for carrying out the molecular modeling of larger sized system computers are needed. ...
Article
Full-text available
Various drug discovery programs have employed molecular modeling methods in pharmaceutical research for studying the complex chemical and biological systems. Experimental and computational strategies when used in combination, plays a very important role in the development and identification of novel compounds. The method of molecular docking is being broadly utilized in modern drug design, for exploring the confirmation of ligand within the target's binding sites. With molecular docking the binding energy of the ligand with receptor can also be estimated. For this purpose, various docking algorithms are available nowadays and it is very important to understand the limitations and advantages of each of these methods for developing efficient strategies and for generating appropriate results. In this review the basics of computational drug designing process has been discussed along with introduction molecular docking method and its application in drug discovery and medicinal chemistry.
... Such targets would allow preventing a switch from the proliferative to the hypertrophic phenotype in the case of cartilage degenerative diseases such as osteoarthritis. Correcting aberrant cellular behavior with drugs requires knowledge about multiple interacting signaling proteins, which necessitates the use of computer tools (Kumar et al., 2006;Hopkins, 2008;Voit, 2012). The last section of this paper illustrate the benefits that network inference from experimental data could bring to predictive computational models. ...
Article
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The specialization of cartilage cells, or chondrogenic differentiation, is an intricate and meticulously regulated process that plays a vital role in both bone formation and cartilage regeneration. Understanding the molecular regulation of this process might help to identify key regulatory factors that can serve as potential therapeutic targets, or that might improve the development of qualitative and robust skeletal tissue engineering approaches. However, each gene involved in this process is influenced by a myriad of feedback mechanisms that keep its expression in a desirable range, making the prediction of what will happen if one of these genes defaults or is targeted with drugs, challenging. Computer modeling provides a tool to simulate this intricate interplay from a network perspective. This paper aims to give an overview of the current methodologies employed to analyze cell differentiation in the context of skeletal tissue engineering in general and osteochondral differentiation in particular. In network modeling, a network can either be derived from mechanisms and pathways that have been reported in the literature (knowledge-based approach) or it can be inferred directly from the data (data-driven approach). Combinatory approaches allow further optimization of the network. Once a network is established, several modeling technologies are available to interpret dynamically the relationships that have been put forward in the network graph (implication of the activation or inhibition of certain pathways on the evolution of the system over time) and to simulate the possible outcomes of the established network such as a given cell state. This review provides for each of the aforementioned steps (building, optimizing, and modeling the network) a brief theoretical perspective, followed by a concise overview of published works, focusing solely on applications related to cell fate decisions, cartilage differentiation and growth plate biology. Particular attention is paid to an in-house developed example of gene regulatory network modeling of growth plate chondrocyte differentiation as all the aforementioned steps can be illustrated. In summary, this paper discusses and explores a series of tools that form a first step toward a rigorous and systems-level modeling of osteochondral differentiation in the context of regenerative medicine.
... Conventional drug discovery processes require huge time and a hefty amount of money to bring a single drug into the market. Hence, in silico methods play a vital role in drug discovery and development by offering an enhanced efficiency to the pharmaceutical industry 1 . Over the last four decades, the drug manufacturing companies have shifted their focus from the reductionist approach towards 'one drug, multiple targets' also known as polypharmacology. ...
Article
Drug repurposing has gained mass recognition over the past few years as it has paved new therapeutic applications for already approved FDA drugs. It focuses on finding new molecular targets of drugs for medical uses different than the one originally proposed. Ceritinib, an Anaplastic Lymphoma Kinase (ALK) inhibitor is given orally in the treatment of non-small cell lung cancer (NSCLC). This treatment has been reported to be associated with a number of side effects such as hyperglycemia, convulsion, pneumonitis etc. The side effects are usually due to the unintended interaction of the drug with other protein targets. In silico polypharmacological studies of Ceritinib suggests that it binds to multiple targets other than the intended one which may largely be due to different proteins possessing similar binding sites. ProBis server was used to retrieve probable off-targets of Ceritinib based on presence of structurally similar protein binding sites as that of ALK. Ceritinib was found to bind effectively to three proteins namely Lymphocyte Cell-Specific Protein-Tyrosine Kinase, Tropomyosin receptor kinase B and Aurora kinase B having favorable binding energies and inhibition constants, with no reported side-effects as compared to their marketed drugs. Therefore, it is concluded from the present study that Ceritinib may act as an effective therapeutic target against its polypharmacological targets.
... Studies suggest that the immunological immaturity of infants and young children limits the induction/persistence of long-lived plasma cells [2] and, for this reason, a booster dose is needed. The same occurs with the elderly due to immunosenescence. 1 In the vaccinology field, computer tools have been used to assist the vaccine development process [4][5][6][7][8][9][10][11][12][13]. Several computational modelling techniques can be used to achieve this objective [14]. ...
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Background An effective yellow fever (YF) vaccine has been available since 1937. Nevertheless, questions regarding its use remain poorly understood, such as the ideal dose to confer immunity against the disease, the need for a booster dose, the optimal immunisation schedule for immunocompetent, immunosuppressed, and pediatric populations, among other issues. This work aims to demonstrate that computational tools can be used to simulate different scenarios regarding YF vaccination and the immune response of individuals to this vaccine, thus assisting the response of some of these open questions. Results This work presents the computational results obtained by a mathematical model of the human immune response to vaccination against YF. Five scenarios were simulated: primovaccination in adults and children, booster dose in adult individuals, vaccination of individuals with autoimmune diseases under immunomodulatory therapy, and the immune response to different vaccine doses. Where data were available, the model was able to quantitatively replicate the levels of antibodies obtained experimentally. In addition, for those scenarios where data were not available, it was possible to qualitatively reproduce the immune response behaviours described in the literature. Conclusions Our simulations show that the minimum dose to confer immunity against YF is half of the reference dose. The results also suggest that immunological immaturity in children limits the induction and persistence of long-lived plasma cells are related to the antibody decay observed experimentally. Finally, the decay observed in the antibody level after ten years suggests that a booster dose is necessary to keep immunity against YF.
... Both computational and experimental techniques have important roles in drug discovery and development and represent complementary approaches. The key role of CADD is to use of computing power to restructure drug discovery and development process [4]. It also leverage of chemical and biological information about ligands and/or targets to identify and optimize new drugs. ...
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Reliability and fault free environment is always a key important factor in any computer aided system. The one key factor is software fault. Faults can be reside and arise anywhere anytime though it is very well tested. In life critical systems/applications, fault must be tolerated at any cost.. Drug discovery and development are very time and resources consuming processes. It is very challenging era for today's market. Computational techniques to the combined chemical and biological space with the purpose of simplify drug discovery, design, development and optimization. In this research, Software fault tolerance is adopted with drug designing for high reliability. Computational tools and techniques are very popular in pharmaceutical industry to increase the efficiency of drug discovery and development process. Software fault tolerance has many techniques to achieve high reliability in any system. These techniques generally base on diversy fundamentals. Such diversy works on data, specification, design, component, environment and resource. The diversity approach works better for high reliability when we ensure that diversity is applicable to all possible components. There are many alternatives, out which at least one alternate option would be execute and take responsibility of fault handling. Firstly, it introduces software fault tolerance techniques and drug designing procedures. This research focuses on a high reliability in computer aided drug designing by fault forecasting SFT Model. The proposed SFT model includes feasible study, requirement gathering-specification logging, specification and design fault matching and developing along with code diversity fundamentals. On occurrence of faults it will follow specific diversy to provide high availability and reliability. Design defects of any CADD software will cause a system failure as unsolvable condition. To provide alternation to such system, only code diversy is not enough. In the direction of high available and fault free CADD software the appropriate system design is necessary with multiple diversy approach. Reliability and fault forecasting SFT model will be monitoring the alternative for design-code-development at any faulty state. Applying SFT techniques in CADD software, execution throughput will higher by tolerating various faults with diversy fundamental. The proposed fault forecasting model will increase reliability in computer aided drug designing and also predicts the faults associated with concern inputs and procedures. This proposed SFT model will be useful to forecast faults for future CADD systems.
... Computational methods are now widely used throughout the DDP to yield better-informed decisions. Indeed, such methods have the potential of saving millions within the DDP (Kumar et al., 2006). For example, pharmacokinetic (PK) modeling can save resources and expedite the DDP by reliably predicting in-vivo Absorption, Distribution, Metabolism, and Excretion (ADME) properties of a drug (Gallo, 2010). ...
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Computational methods have provided pharmaceutical scientists and engineers a means to go beyond what's possible with experimental testing alone. Providing a means to study active pharmaceutical ingredients (API), excipients, and drug interactions at or near-atomic levels. This paper provides a review of this and other innovative computational methods used for solving pharmaceutical problems throughout the drug development process. Part one of two this paper will emphasize the role of computational methods and game theory in solving pharmaceutical challenges.
... To enable using CD nano-carrier system for a novel class of drugs, one should perform a screening and/or biochemical characterization of potential complexes, which can be a quite laborious and expensive process [28]. Computational methods aim to automatize and reduce the time and cost to discover new drugs [18,15]. However, traditional scoring functions (SF), empirical or physics-based, still struggle to rank potential binders unless rigorous free energy calculations could be performed. ...
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... To enable using CD nano-carrier system for a novel class of drugs, one should perform a screening and/or biochemical characterization of potential complexes, which can be a quite laborious and expensive process [28]. Computational methods aim to automatize and reduce the time and cost to discover new drugs [18,15], however, traditional scoring functions (SF), empirical or physics-based, still struggle to rank potential binders, unless rigorous free energy calculations could be performed, this limitation has been addressed in several community challenges [24], highlighting the potential of Machine Learning Scoring Functions (MLSF) to improve predictions. Therefore, it is crucial to develop models for pre-screening to direct rigorous tests. ...
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Machine Learning (ML) techniques are becoming an integral part of rational drug design and discovery. Data-driven modeling regularly outperforms physics-based models for predicting molecular binding affinities, placing ML as a promising tool. Cyclodextrins are nano-cages used to improve the delivery of insoluble or toxic drugs. Due to chemical similarity to proteins, ML approaches could vastly profit to improve affinity prediction and enhance their carriable drug portfolio. Here we evaluate the performance of the Gaussian Process Regression (GPR) to predict the binding affinity of cyclodextrin and known ligands. GPR performance is compared with two well-known ML methods - Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGB). We perform hyperparameter tuning through a Random Search strategy. GPR was able to increase the prediction performance when compared to SVR and XGB, leading to better performance to adjust the data ($R^2$ = 0.803) with low prediction errors (RMSE = 1.811 kJ/mol and MAE = 1.201 kJ/mol).
... There are many reasons for this trend: first is the prohibitive cost (estimated at around $2.6 billion) to develop a single drug [3,4]; then the huge investment of time as the complete process of drug discovery takes around 14 years [5]; according to a rough estimate from phase I to FDA approval, only 15 percent of the compounds make it, rest are rejected [6]. Because of all these reasons, gradually, the trend is to shift towards in-silico medicine [7][8][9][10][11][12][13][14]. This is true for anti-cancer compounds also. ...
Chapter
Cancer is one of the leading causes of death globally and the number of cancer patients is increasing. The highly complex nature of this disease and non-specificity of anti-cancer drugs is a major issue in the treatment of cancer. The advent of high-speed processing units and sophisticated molecular modeling software has completely altered the landscape of drug discovery in cancer research. This review focuses on the importance and latest advances of in silico modeling for the design of new and potent anti-cancer drugs. Though in silico methods have transformed the growth and design of small molecule anti-cancer drugs, but acquired resistance and intra-tumor heterogeneity are still challenges for which solutions need to be sought. Also, there is a shift from the ‘One Ligand-One Target’ approach to the ‘Multi Target Drug Ligands’ (MTDL)’ approach for drug design for treating cancer.
... In the vaccinology field, computer tools have been used for a long time to assist the vaccine development process [16]- [26], using distinct computational modeling techniques to achieve this objective [27]. Most of them focus on non-clinical trials, although computer tools can also assist researchers in the clinical development stage [28]. ...
Conference Paper
The human immune system (HIS) is responsible for the defense of the organism against pathogens. Mathematical models can be a useful tool to study different aspects of HIS through in silico trials. In this work, we investigate both primary and secondary responses to the Yellow Fever virus (YFV) using mathematical models. Uncertainty quantification and sensitivity analysis are performed via Monte Carlo and polynomial chaos expansion methods. It was possible to reproduce key aspects of antibody dynamics in a secondary response to the YFV and quantity the influence of model parameters in relevant phases of the immune response.
... Over the last two decades, significant strides have been made in applying computational approaches across the full spectrum of drug development. 37 In their many forms, computational tools can include discovering new lead candidates with optimal drugreceptor binding affinity (e.g. Quantitative Structural Activity Relationships (QSAR)), to guiding on optimal physiochemical profiles (e.g. ...
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... However, PBL can be shipped to a central monitoring lab with reasonable viability. Mathematical modelling of the activity of cancer vaccines is a relatively nascent stage [86][87][88], but may provide a promising avenue to estimate the interactivity of the various components of cancer vaccines. a. Chemotherapy: the vast majority of chemotherapies are designed to eliminate rapidly dividing cells, which unfortunately is also a major characteristic of an activated immune response! ...
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The capacity of the immune system to influence tumor progression has been a long‐standing notion that first generated clinical traction over a hundred years ago when Dr William Coley injected disaggregated bacterial components into sarcomas and noted that the ensuing inflammation commonly associated with tumor regression [1]. Since then, our understanding of the individual components and the overall interaction of the immune system has expanded exponentially. This has led to the development of a robust understanding of how components of innate and adaptive immunity recognize and respond to tumors and leveraging this information for the development of tumor immunotherapies. However, clinical failures have also deepened our knowledge of how tumors might adapt/be selected to avoid or inhibit immune responses, which in turn has led to the further iteration of immunotherapies. In this tutorial, the established elements of tumor immunity are explained, and areas where our knowledge base is too thin is highlighted. The principles of tumor immunity that guide the development of cancer vaccines are further illustrated, and potential considerations of how to integrate cancer vaccines with conventional therapies and other immunotherapies are proposed.
... Further, as this virus is novel, so, till date, neither vaccination nor any drug available to treat this disease. Discovery of a new drug even though the silico-chemicobiological approach [1,2] is not advisable in the present situation because of time constraints and fatality rates. This outbreak of coronavirus disease 2019 (COVID-19) triggered by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a severe threat to worldwide public health. ...
... 11 All these techniques are becoming very important part of drug discovery and development process and it is expected that the computational method can help in improving the efficiency for the industry by decreasing the requirement of resources. 12 Molecular docking may be defined as an optimization process, which would describe the "best-fit" orientation of a ligand that binds to a particular protein of interest. However since both the ligand and the protein are flexible, a "hand-in-glove" analogy is more suitable than "lock-and-key". ...
... Computational modeling in drug discovery has been used for some time in industry. Because of the highly competitive landscape and the large economic incentives, drug discovery is the strongest driver in the development of cheminformatics and data-driven tools in chemistry, including data integration [116,117]. An early analysis of the integration of data and knowledge in drug discovery is given in the review paper of Searls [118]. ...
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Thesis
Traditional canonical signaling pathways help to understand overall signaling processes inside the cell. Large scale phosphoproteomic data provide insight into alterations among different proteins under different experimental settings. Our goal is to combine the traditional signaling networks with complex phosphoproteomic time-series data in order to unravel cell specific signaling networks. On the application side, we apply and improve a caspo time series method conceived to integrate time series phosphoproteomic data into protein signaling networks. We use a large-scale real case study from the HPN-DREAM BreastCancer challenge. We infer a family of Boolean models from multiple perturbation time series data of four breast cancer cell lines given a prior protein signaling network. The obtained results are comparable to the top performing teams of the HPN-DREAM challenge. We also discovered that the similar models are clustered to getherin the solutions space. On the computational side, we improved the method to discover diverse solutions and improve the computational time.
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This work aims to implement specific image processing methodologies to aid the appropriate diagnosis of lung cancer and provide a sterile environment for interacting with the medical data in operation theatre during treatment. Computer Tomography (CT) gives a good resolution axial slice image of the lung. The tumor’s raw CT data analysis is quite impossible as the tumor’s pixel characteristics would be approximately matching its neighboring pixels. Thus, a basic preprocessing algorithm is required to differentiate target from the background. It is processed with level set segmentation to extract tumor from the sequentially acquired multiple slices. The segmented output of each slice provides the geometric feature of the tumor. As the tumor size and shape in segmented portions are irregular, it seems appropriate to reconstruct a three-dimensional representation of 2D images for tumors’ qualitative information. The volume reconstruction using the ray casting method has been applied to render the volume of tumors from a segmented stack of 2D slices. Then touch-less, computer-aided, gesture-based control of the medical images was attained using Kinect sensor, which is the best tool for human-computer interaction to maintain a sterile environment. The paper elaborates on the gestures, which is employed to interact with the medical images such as selection, drag, and swipe gesture. The selection gesture is used to open and close individual medical images. The drag gesture was used to view the patient data along with the slice image. The swipe gesture used to view various medical data sets like 2D slices, tumor irregularity in segmented slices.
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The focus of innovation in current drug discovery is on new targets, yet compound efficacy and safety in biological models of disease, not target selection, qualify drug candidates for the clinic. We consider a biology-driven approach to drug discovery based on screening compounds by automated response profiling in complex human cell systems-based disease models. Drug discovery through cell systems biology could significantly reduce the time and cost of new drug development.
Building with a scaffold: emerging strategies for high- to low-level cellular modeling
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Ideker, T. and Lauffenburger, D. (2003) Building with a scaffold: emerging strategies for high-to low-level cellular modeling. Trends Biotechnol. 21, 255–262
Combined Signaling through ERK, PI3K/AKT, and RAC1/p38
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Segarra, J. et al. (2006) Combined Signaling through ERK, PI3K/AKT, and RAC1/p38