Seth Michelson

Integrative Systems Biology II—Molecular Biology: Phase 2 Lead Discovery and In Silico Screening

Many different OMICs/HTS techniques now allow huge amounts of molecular signatures to be collected and then analysed further by system tools. Among them, ChIP-on-chip is used to investigate interactions between proteins and DNA in vivo. Chemogenomics, morphogenics and synthetic biology are only in the early stages of development, but may contribute to target identification. A key SB tool, the reconstruction of biological networks, represents an emerging field, undergoing explosive expansion, and will likely enable efficient mapping of gene onto function.

Development: Multiscale CSB—Simulation Tools

In order to cover bottom-up and top-down phenomena multiscale SB simulation tools should include organ-level considerations, and should be used in conjunction with multiscale modeling tools which have the ability to handle many orders of magnitude in both length and timescale. Several new R&D paradigms, based on CSB, are proposed, while some are already in the research stage. This effort will lead to virtual organ/disease models, emerging as important tools. Identifying and targeting a system’s emergent properties is a major goal for coming years. This will cause a paradigm shift in R&D activity in Pharma yielding a move from population models to models of individualized medicine. The importance of multiscale CSB is underlined here as a great attention is given here in this section.

Discovery: Computational Systems Biology (CSB) in Health and Disease I

To date, few cellular and gene networks have been reconstructed and analyzed in full. Examples include some prokaryotes and few eukaryotes for cellular networks. The methods currently used to analyze single database genomic sets are usually mature and refined. Network reconstruction is also enabled by analysing the molecular connectivity of a system by using correlation analysis. Additionally, monitoring the dynamics of the system and measuring the system’s responses to perturbations such as drug administration or challenge tests can yield insights into the dynamics of the system. Microbial cells are fairly well characterized, but the status of similar efforts for mammalian cells is rather poor. While emergence can be conveniently studied via computational tools, the phenomenon of emergence is the single most important benefit of CSB.

Integrative Systems Biology I—Biochemistry: Phase I Lead Discovery and Molecular Interactions

This chapter represents a mix of reductionist and holistic tools. Molecular screens and Biomimetics represent advanced reductionist approaches—the former are well established in the industry, although still developing. Similarly, the collateral efficacy/permissive antagonism concept may add to this effort, possibly generating new targets. Solving different co-drugging modalities represents a typical SB approach. Likewise, text mining does add to the holistic (global) effort. Tools to analyze biochemical networks and the phenomenon of emergence may lead to the establishment of ‘new biology’ or computational systems biology (CSB). Reactome analysis and bioinformatics tools only reinforce this effort. The level of development of the above quantitative tools is not uniform: some are advanced and mature (e.g., molecular screens), some require more inputs and are undergoing rapid evolution.

Development: In Vivo Pharmacology—Systems Biology in Health and Disease II

This chapter covers qualitative in vivo approaches in animals and man, which will help to develop in silico pharmacology and PK positions. Additionally, we cover RNA interference in this chapter even though it is largely an in vitro method for characterizing the dynamics of cell physiology. And though in silico pharmacology is only in a rudimentary state, it is vitally important for clinical model based drug design (MBDD) development (see Chap. 10).

Systems Biology: Impact on Pharma and Biotech

SB is important for DD because it can be used to rapidly identify the MoA of novel drugs, enabling companies to make go/no decisions earlier in the drug development process by avoiding pathways associated with toxicological or pharmacological issues. SB can reduce the number of compounds synthesized and manufactured owing to refined algorithms which avoid poor PK and toxic effects. In the longer term investments in SB will enable research institutions and companies to save time and money in the DD process by choosing drugs which are more likely to succeed in clinical development.

Development: Preclinical Model Based Drug Development

Large scale in silico clinical development will only become a reality after some effort is exerted; some partial solutions (SB and BI tools mentioned in previous chapters) already exist and first attempts have been made. We acknowledge here that we are NOT in square zero and that in silico technologies have been, and are already being, used in clinical trial design and execution, used in simulation studies for adaptive trials, and used to identify patient subpopulations and markers for enrichment strategies. This goal will require a concentrated effort by all players, with considerable investment from the pharma industry and governments.

Introduction: Discovery and Development—New Facet of Industry, New Tools and Lead Optimization

In the face of the challenges associated with expiring drug patents, the rising cost of R&D, and payer pressure on pricing, most major pharmaceutical companies are seeking ways to enhance productivity, reduce costs and augment their late-stage pipelines. Recent technological applications have witnessed the development of data-rich, genome-scale functional screens, large collections of reagents and protein microarrays, and the addition of databases and algorithms for data mining. Systems biology takes advantage of these technological breakthroughs to represent a combination of reductionist and holistic approaches to the relationships among the elements of a system.

Development: Pharmacokinetics—Systems Biology in Health and Disease III

In silico PKPD/ADMET and biochemical-mechanistic methods will become a standard approach in the coming few years via the employment of BI and SB tools at the multiscale whole-body level. So far, the overall impact of toxicity markers on preclinical safety testing has been modest. The greatest benefit of PBPK models is they may allow for individualized health care.

Discovery: Use of Systems Biology for Identifying Targets

In our introduction, we emphasized that a combination of reductionist (mechanism-based) and holistic (hypothesis-based) tools in the drug screening process may increase the efficiency of overall Drug Discovery. Among notable holistic tools are screens that target discovery and characterization of molecular probes (compounds) that will enable the investigation of fundamental biological function at molecular, cellular and whole organism levels. Such screening usually occurs at the earlier stages of drug discovery.