At the Biological Modeling and Simulation Frontier (original) (raw)
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Synthetic Biology: Computational Modeling Bridging the Gap between In Vitro and In Vivo Reactions
Current synthetic & systems biology, 2015
The synthetic biology firstly refers to the design and fabrication of biological components and systems that do not already exist in the natural world and to the redesign and fabrication of existing biological systems. The link of computational tools to cell-free systems, converts to synthetic biology is an emerging field expert to build artificial biological systems through the combination of molecular biology and engineering approaches. Herein, most findings describing the differences between in vivo and in vitro reactions and systems have been extensively described. The specific applications of computational tools to the design of an in vitro gene expression platform known as the artificial cell, its components and the strategies developed to predict activities of processor modules and to control the expression of genes have been discussed in detail. Potential applications of artificial cells in drug delivery, in biosynthesis, among others, have been described. Two sources of models for the possible developing of the computational toolbox for cell-free synthetic biology include i) Physical models of single cellular components able to be created from original principles, guiding to focus on tools to predict structure and dynamics of particular components; ii) A wide-range of mathematical models for predicting system dynamics of natural cells. Regarding modeling algorithms, there is a broad kind of models available for synthetic biologists and some areas of potential growth identified for researchers interested in developing tools for cell-free systems. Among them, deterministic, exploratory, molecular dynamic, stochastic, all atom models, among others, have been described and discussed. By using computational models to set up quantitative differences between in vitro reactions and in vivo systems, could identify specific mechanisms in living organisms to be further used in in vitro reactions in order to facilitate their processes. Thus, computational modeling would bridge the gap between in vitro and in vivo reactions.
The current role of model-based drug development
Expert opinion on drug discovery, 2010
Current drug discovery and development programs are under growing scrutiny for low productivity and escalating costs. Model-based drug development (MBDD) has been recognized as a promising tool to address some of the related challenges. This review introduces the concept of MBDD and the associated quantitative pharmacology-based iterative 'learn and confirm' paradigm in the drug discovery and development process to provide concise information for rational decision making. It summarizes the evolving role of MBDD in drug development programs and outlines the full armamentarium of modeling and simulation approaches utilized to facilitate its application. Different aspects and applications of MBDD are introduced to the reader and illustrated in prime examples. The reader is provided with an understanding of potential applications of MBDD in drug development as well as the associated limitations and challenges in its implementation. MBDD is a tool that is increasingly used throug...
Applying computational modeling to drug discovery and development
Drug Discovery Today, 2006
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. Reviews INFORMATICS
Moving beyond in silico tools to in silico science in support of drug development research
Drug Development Research, 2011
Exploitation of concretized mechanistic models and simulation methods enables acquiring a competitive advantage through deeper, easily shared, mechanistic insight into the disease and/or health phenomena that are the focus of the R&D organization. The models are analogues of the biological wet-lab models used to support that R&D. An analogue is an explanatory and evolving hypotheses about the mechanistic consequences of xenobiotic or biologic interventions. As such they are fundamentally different from the familiar inductive, equation based, pharmacokinetic, pharmacodynamic, and related models. Analogues are designed for experimentation and to be useful in the face of incomplete data and multiple uncertainties. They use interchangeable components and require iterative refinement. They enable linking coarse-grained systemic phenomena to fine-grained molecular details, including molecular targets. To simplify and focus discussion we describe one example of the new class of models, In Silico Livers. We present a vision of how the biological wet-lab side of the R&D process might function when these models and methods are fully implemented within a common computational framework. Accumulated mechanistic knowledge is easily measured and visualized in action, and therefore it can be easily challenged. Components within analogues that have been validated for many compounds can use programmed "intelligence" to automatically parameterize for, and respond to, a new, not previously seen compound based on its physicochemical properties. Each analogue can be tuned to reflect differences in experimental conditions and individuals, making translational research more concrete while moving us closer to personalized medicine.
Computing and Philosophy: Selected Papers from IACAP 2014, 2014
Computer simulations constitute a significant scientific tool for promoting scientific understanding of natural phenomena and dynamic processes. Substantial leaps in computational force and software engineering methodologies now allow the design and development of large-scale biological models, which – when combined with advanced graphics tools – may produce realistic biological scenarios, that reveal new scientific explanations and knowledge about real life phenomena. A state-of-the-art simulation system termed Reactive Animation (RA) will serve as a study case to examine the contemporary philosophical debate on the scientific value of simulations, as we demonstrate its ability to form a scientific explanation of natural phenomena and to generate new emergent behaviors, making possible a prediction or hypothesis about the equivalent real-life phenomena. 9.1 Introduction Computer simulations constitute a significant scientific tool for promoting scientific understanding of natural phenomena and dynamic processes in diverse disciplines, including biology. The need of culling significant knowledge and insights from vast amounts of empirical data, generated in recent decades about biological molecules and the millions of interactions among them, has promoted the development of innovative sophisticated computational methods and helped form new interdisciplinary research fields. A group of researchers have developed over the last decade a computational approach termed Reactive Animation (RA) for simulating complex biological systems (Vainas et al. 2011). The dynamic characteristics of the biological objects are described based on cellular and molecular data collected from lab experiments. These data are integrated bottom-up by computational tools and methods to create a comprehensive, dynamic, interactive simulation (with front-end animated
Multi-step usage of in vivo models during rational drug design and discovery
International journal of molecular sciences, 2011
In this article we propose a systematic development method for rational drug design while reviewing paradigms in industry, emerging techniques and technologies in the field. Although the process of drug development today has been accelerated by emergence of computational methodologies, it is a herculean challenge requiring exorbitant resources; and often fails to yield clinically viable results. The current paradigm of target based drug design is often misguided and tends to yield compounds that have poor absorption, distribution, metabolism, and excretion, toxicology (ADMET) properties. Therefore, an in vivo organism based approach allowing for a multidisciplinary inquiry into potent and selective molecules is an excellent place to begin rational drug design. We will review how organisms like the zebrafish and Caenorhabditis elegans can not only be starting points, but can be used at various steps of the drug development process from target identification to pre-clinical trial mode...
Mechanistic systems modeling to guide drug discovery and development
Drug Discovery Today, 2013
A crucial question that must be addressed in the drug development process is whether the proposed therapeutic target will yield the desired effect in the clinical population. Pharmaceutical and biotechnology companies place a large investment on research and development, long before confirmatory data are available from human trials. Basic science has greatly expanded the computable knowledge of disease processes, both through the generation of large omics data sets and a compendium of studies assessing cellular and systemic responses to physiologic and pathophysiologic stimuli. Given inherent uncertainties in drug development, mechanistic systems models can better inform target selection and the decision process for advancing compounds through preclinical and clinical research. Computational methods have made exciting contributions to pharmaceutical research and development. Computer-aided drug design has been established as a valuable tool for the design of new drugs, with many success stories since the 1980s [1]. Pharmaceutical companies have invested substantially in bioinformatics approaches, and it has been predicted such approaches will have an important role in pharmacogenomics and personalized medicine [2]. Already, the FDA has recognized the importance of informatics approaches to generate novel biomarkers to personalize cancer therapies [3]. Mechanistic modeling approaches can yield insights from data throughout the drug development process. For example, in the context of metabolomics, it is well-established that systems models facilitate insights from high-throughput data [4]. Even when models are not specifically constructed for pairing with high-throughput data, they can be informed from the literature and preclinical studies. Much of the utility of systems modeling for advancing therapeutics lies in the ability to develop hypotheses regarding the characteristics of a disease system. Such approaches to pharmaceutical research parallel systems biology. They are driven by the ability to formulate testable hypotheses, are inherently quantitative because they use a quantitative modeling framework, integrate potentially high dimensional data from multiple sources, and enable global mechanistically based analysis of the physiologic system [5]. Notably, such integrative approaches can assist in translating a result from an in vitro study or animal model to better predict efficacy in a clinical context.
Extrapolating from Model Organisms in Pharmacology
Boston Studies in the Philosophy and History of Science, 2020
In this paper we explore the process of extrapolating causal claims from model organisms to humans in pharmacology. We describe and compare four strategies of extrapolation: enumerative induction, comparative process tracing, phylogenetic reasoning, and robustness reasoning. We argue that evidence of mechanisms plays a crucial role in several strategies for extrapolation and in the underlying logic of extrapolation: the more directly a strategy establishes mechanistic similarities between a model and humans, the more reliable the extrapolation. We present case studies from the research on atherosclerosis and the development of statins, that illustrate these strategies and the role of mechanistic evidence in extrapolation.
Challenges and opportunities with modelling and simulation in drug discovery and drug development
Xenobiotica, 2007
The benefits of modelling and simulation at the pre-clinical stage of drug development can be realized through formal and realistic integration of data on physicochemical properties, pharmacokinetics, pharmacodynamics, formulation and safety. Such data integration and the powerful combination of physiologically based pharmacokinetic (PBPK) with pharmacokinetic-pharmacodynamic relationship (PK/PD) models provides the basis for quantitative outputs allowing comparisons across compounds and resulting in improved decision-making during the selection process. Such PBPK/PD evaluations provide crucial information on the potency and safety of drug candidates in vivo and the bridging of the PK/PD concept established during the pre-clinical phase to clinical studies. Modelling and simulation is required to address a number of key questions at the various stages of the drug-discovery and -development process. Such questions include the following. (1) What is the expected human PK profile for potential clinical candidate(s)? (2) Is this profile and its associated PD adequate for the given indication? (3) What is the optimal dosing schedule with respect to safety and efficacy? (4) Is a food effect expected? (5) How can formulation be improved and what is the potential benefit? (6) What is the expected variability and uncertainty in the predictions? Figure 1. Physiologically based pharmacokinetic model (PBPK).