ForewordQuantitative and Systems Pharmacology Workshop Executive SummaryGroup 1: Horizontal System Integration in PharmacologyGroup 2: Vertical Integration in PharmacologyGroup 3: Quantitative Biology and PharmacologyGroup 4: Education TrainingGroup 5: Data Management - Overall ImpressionsAddendum: Planning Document
Quantitative and Systems Pharmacology Workshop held on September 25-26, 2008 sought to address the question of where systems biology, modeling, and more quantitative measurements could be applied to pharmacology and drug discovery/action now and in the foreseeable future. Meeting goals included highlighting where the state-of-the-art resides in relevant disciplines today, fostering integration of research efforts in these areas with others, identifying barriers (problems merging two disciplines), increasing collaboration between pharmacologists, clinicians, and systems biologists, and establishing pressing and long-term research needs (training, instrumentation, algorithms, etc) for advancement of the ability of systems biology to inform drug discovery and drug action. The meeting report that follows includes an executive summary of the workshop authored by Douglas Lauffenburger, who along with William Jusko, co-chaired the workshop. The executive summary is followed by reports from each of the five-breakout session co-authored by the discussion leader and recorder for each session. (Group I: Michael Phelps and Reka Albert; Group II: David D'Argenio and Rai Winslow; Group III: Juan Lertora and Peter Sorger; Group IV: Henrik Dohlman and Fei Hua; Group V: Yoram Vodovotz and Avi Maayan) Finally, also included is a copy of the pre-meeting planning document that assembled and recorded community input on the areas covered by the workshop; this was authored by NIH staffers, Peter Lyster and Sarah Dunsmore.
NIH staff have begun planning for a follow up workshop tentatively titled Quantitative and Systems Pharmacology II.
D.A. Lauffenburger, Massachusetts Institute of Technology
The Quantitative and Systems Pharmacology Workshop, held 25-26 September 2008 at the
NIH campus, aimed to provide state-of-the-art-knowledge and perspectives on the interface of systems biology and pharmacology to a highly diverse spectrum of researchers in academia, industry, and government. Addressing the question of where systems biology, modeling, and more quantitative measurements can be applied to pharmacology and drug discovery/action now and in the foreseeable future was the central focus of talks and discussion sessions. Five major aspects of the systems biology/pharmacology interface were explicitly featured:  horizontal systems integration;  vertical systems integration;  quantitative biology and pharmacology;  data management;  education and training.
Widespread appreciation arose for the need to create mechanisms for bringing the systems biology and pharmacology communities together more frequently and deeply, because of the clear promise for common benefit. The complexity inherent in understanding and predicting the pathophysiology and drug effects in patients ought to be gainfully attacked by including the methods and concepts being developed by systems biology investigators and, in turn, the envisioned impact of systems biology ought to be pursued vigorously in this important realm of molecular medicine. The arena of systems biology would benefit from greater appreciation of the diverse complexities inherent in the actions of many drugs. Systems and homeostatic perturbations induced by drugs can serve as probes of the validity of systems models.
One dimension of the scientific challenge is the need for more comprehensive 'horizontal integration'. That is, even at the level of cellular pharmacodynamics, understanding drug action requires integration of effects within the myriad interconnecting multi-pathway networks of signaling, metabolism, and gene expression; moreover, operation of these pathways involves molecular communication across cell boundaries into the microenvironment. Hence, quantitative measurement and modeling must emphasize incorporation of multi-pathway information and in tissue environmental context, which will require major advances mainly in experimental technologies including tissue-level imaging with molecular resolution. The second dimension of scientific challenge naturally follows, the need for more explicit 'vertical integration', in which "bottom-up" models rooted in detailed molecular mechanisms at the cellular level must meet "top-down" models describing organ- and organism-level physiology with the objective of the higher-level observations be interpretable and predictable more explicitly in terms of measurable molecular and cellular properties. This will require advances mainly in computational methodologies via which consequences of multi-pathway molecular mechanistic detail at the lower level can be propagated efficiently as the scope escalates up from cells to tissues to organs to organisms. A high priority goal is to leverage the systems approach to shift the basis for biomarkers from correlative to mechanistic, and likely multi-variate, linking quantitative biology to quantitative pharmacology. Because of practical constraints on proving and achieving the aspired combination of horizontal and vertical integration in human patient studies, one approach proffered is dedicated full-scale tests in animal studies. Although successful accomplishment of an envisioned quantitative systems pharmacology paradigm therein would not have short-term impact in the clinic, it would give confidence in the effective utility of the new paradigm. A third, complementary challenge arises from the problem of managing—i.e., organizing, storing, accessing, and visualizing—the various kinds of experimental data in concert with the diverse classes of computational models that attempt to capture their salient features. This problem is dissimilar to the more straightforward issue previously experienced by the genomics field, in which data can be relatively easily managed due to its fairly homogenous structure. The immense degree of heterogeneity of horizontally- and vertically-integrated data for drug actions within complex molecular networks for understanding and prediction of organ- and organism-level physiological behavior will require coupled data- and modeling-management approaches that cannot currently be found "on the shelf". Finally, there was essentially unanimous recognition that a new cohort of scientists and engineers will need to be educated at the systems biology / pharmacology interface, as the current population overlap is vanishingly small. Consensus landed on postdoctoral training as the most effective locus for dedicated educational efforts, because of the need to have a strong research capability foundation first before tackling the daunting inter-disciplinarities involved. One unusual avenue gained significant favor, that of multi-institutional postdoctoral training programs, since few institutions possess the necessary expertise across the many contributing fields.
As a working definition of Horizontal Systems Integration we refer to the integration of multiple interconnected events at the molecular mechanism to cellular outcome (phenotype) level of pharmacology. The system may involve, for example, multiple signaling or metabolic pathways and the interconnections of signaling to metabolic pathways; multiple normal & disease cell types; communication between cells to execute multi-cellular functions; and signaling, metabolism and cellular response to drug treatments to define therapeutic effectiveness, as well as compensatory and refractory responses. The phenotype and biological activity of these systems is the target of pharmacological intervention, and is the target of
in vitro and
in vivo (imaging) molecular diagnostics to read out the differential states of normal vs diseased cells.
Progress towards empowering systems approaches must be based on integrating experimental, modeling, and theoretical approaches. Quantitative and kinetic measurements are critical for understanding the biochemical foundation of these events and identifying molecular targets with a therapeutic index. Emphasis must be placed on developing new measurement technologies for system-wide measurements, and new theoretically founded algorithms for interpreting the resulting data and building a more complete description of the systems. Technology and methodology advances can produce powers of 10 in accelerating and expanding approaches to solving this systems problem. Novel technology platforms will be enabling in creating the measurement sciences necessary to achieve these goals.
The new measurement platforms need to be evolvable and scalable to meet the growing and changing needs of experimentation and implementation of great ideas by great scientists, similar to the revolutionary outcomes resulting from Moore's Law in integrated circuits. Useful characteristics of such technology platforms will include real time measurements, measurements of metabolic reaction rates and fluxes through pathways, and the development of new classes of protein capture agents for moving proteomics techniques towards the scale of technology platforms developed for genomics. The theory, models and algorithms should aim to make system-wide descriptions and results more comprehendible and useful to biologists and clinicians.
This is further detailed in the outline below:
A number of existing efforts, under the rubric of multiscale modeling, are of conceptual and specific relevance to the challenge of vertical integration in understanding drug action. The work being done by investigators under the current NIH Multiscale Modeling PAR should be closely examined and the successes and failures of these efforts should be analyzed to guide efforts in pharmacology (drug discovery and development). (Also, the efforts and reports of the Interagency Modeling and Analys?is Group (IMAG)" can provide important insight.)
Vertical Integration in Pharmacology: Synthesis of knowledge and understanding of drug action at the molecular, molecular complex, sub-cellular, cellular, multi-cell, tissue, organ, multi-organ systems, organism, and population levels.
The prevailing framework for multiscale integration (to the extent it is considered at all by biological scientists) relies on the traditional experimental approach of studying subsystems, which can involve isolating the subsystem from external factors (or attempting to keep them constant) and studying the behavior of the isolated subsystem. Multilevel behavior is then inferred by combining the subsystems. This is largely the approach contemplated and practiced by most involved in the multiscale modeling initiative. The nature of such an isolated subsystem experimental/conceptual approach may limit ones understanding of the overall biological system, especially when the goal is to understand drug action in disease.
An alternate approach to vertical understanding ("integration"), that aims to identify and elucidate the guiding principles of control and communication defining the behavior of an organism across scales is also needed.
An integrative discipline that interfaces between drug discovery and development and uses biological modeling approaches for hypothesis generation and testing, spanning chemical, biochemical, and physiological processes relevant to drug effects (both toxic and therapeutic) in healthy and diseased organisms.
A systems biology approach to the study of drug effects is predicated on the utility of models as effective means of summarizing broad sets of data that can then engage a diverse community of participants for the conceptual understanding of outcomes and further research focused on elucidating underlying mechanisms and identifying new therapeutic drug targets.
Modeling can integrate experimental data derived from
in vitro, and
in vivo studies in animals and humans. It can include computer assisted drug design based on known or modeled drug-target interactions, homeostatic control systems that modulate drug effects at the molecular, cellular, organ, and system levels, pharmacokinetic-pharmacodynamic modeling, disease progression modeling, animal models of disease, and clinical trial simulations. Ultimately, rigorously validated models should be integrative, quantitative, and predictive. They should also account for individual variability in drug response due to environmental and/or genetic influences, including the placebo effect.
A systems biology approach should also result in discovery of novel biomarkers and a shift from correlative to mechanistic biomarkers relevant to the natural history of disease and to therapeutic and toxic drug effects.
These definitions and challenges were read but not discussed.
Defining the new combinations of competencies, masteries, habits of mind and skills required by the future investigators who will successfully use systems approaches to understand diseases and facilitate drug discovery and development. Defining the pedagogies, curricula and activities that will constitute training for these new combinations.
Teaching (medical, pharmacy, as well as graduate students) thinking about biological systems (where vertical and horizontal integration are not constant) as distinct from engineering systems theories (where organization is fixed; and where the theory does not map well to biology) is a major challenge. The following are several reasons.
The discussion points were introduced in the following order:
The group recognized a significant need to expand current educational programs in quantitative pharmacology and pharmacometrics. The group identified several challenges for organizing such training programs, and proposed possible solutions.
As with any biomedical research enterprise, the ideal environment for training in systems pharmacology is likely to be a major research university with a comprehensive health affairs campus, with robust graduate and fellowship-training programs (including postdoctoral and clinical research fellows). Other institutions should not be excluded however. For example MIT lacks a medical school or hospital, but is a center of excellence in systems biology. Therefore, a collaborative training approach should be considered, such as a consortia mentioned below. There should also be a tradition of research excellence and a history of cooperation between the basic sciences (including pharmacology, physiology, biochemistry) and the quantitative sciences including bioinformatics, biostatistics, genomics, biophysics, engineering, computer-sciences.
Students learn much from one another, and should therefore be recruited from all disciplines and trained as a group. Thus students should be drawn from engineering, the quantitative sciences (physics, mathematics, statistics, computer sciences) as well as from the life sciences. In addition, much of the best work is being done in an industry setting. Thus trainees should also be recruited from the ranks of industry, as well as directly from colleges and universities.
There should be a mix of masters, doctoral, and post-doctoral trainees (including clinicians). Post-doctoral trainees with experience in research, and looking to broaden their skills beyond the topic of their doctoral work, can help fill the short-term needs in the field. However, funding for additional postdoctoral training is also needed to encourage and help PhDs enter a new field. A pool of trainees with a Masters degree in systems pharmacology will serve the needs of some sectors in the short term. However students having
only a Masters may in the long run have difficulty "keeping up" with the rapid pace of research. Another possibility is to recruit students that already have an MD or PhD, as is commonly done at schools of public health and epidemiology. Masters students pay tuition, and this could serve as a revenue stream that supports doctoral-level teaching and research.
Broad-based training in systems pharmacology will require a breadth of expertise that may be difficult to find on a single campus, at least for the foreseeable future. Thus training programs should consider organizing consortia that would share development of educational materials, provide teaching in cross-disciplinary topics, and promote collaborative and inter-disciplinary research programs. Consortia should also include industry, given that data and "real-world" experience may be concentrated in industry labs.
The needs are very much driven by industry, and in any case industry will need to participate in designing new curricula and training programs. There is a wealth of experience within industry, which may be drawn upon in filling the ranks of training faculty, for developing collaborations with academics, for hosting visiting scientists and interns, and as a source of students (such as mid-career scientists).
Some students currently in industry will not be in a position to relocate for training. Thus programs should consider developing on-line learning programs. Mechanisms for on-line learning will also facilitate the exchange of information among consortia members.
In addition to the traditional sources for research funding, there is a dire need for new NIH training grants dedicated to interdisciplinary research. Industry may be willing to provide financial support in some cases for cooperative research programs, and also to pay full tuition for their students. There is also a major need on the part of the FDA for individuals trained in pharmacometrics. Thus the FDA might be a funding source for new training programs.
Courses should include a mix of didactic lectures and hands-on practical (laboratory or computer) training. Here is a list of potential courses that might be offered in a systems pharmacology program:
In a sense, the goal of the meeting was to bring old style modeling pharmacology efforts together with the "new" systems biology experimental and modeling advances. However, in order to be more innovative we need to integrate Systems Biology with PK/PD studies and other modeling efforts but it is impractical to be an expert in all those fields. Moreover, there are challenges specific to the integration of data in these two camps. We have diverse datasets and data types, i.e., data from clinical, pharmacological, cellular studies. How can we manage and integrate such data to advance translational research? A consensus of the group was that "there are so many databases... more databases than articles... by the time you learn what is in one database, another appears... it is impossible to be aware of what is available... a lot of efforts are being duplicated because of that." Indeed, there are also many modeling tools that do the same thing, being developed in parallel and without knowledge of the work of others. It was then agreed that this is also why we need more meta-data and exchange standards: This will improve searching, and speed up the process of understanding the content of databases.
One common goal could be to collect data for building unifying models. Alternatively, it may be argued that thinking about models is restrictive since sometimes viewing a collection of facts or exploring networks that display relations between entities can be also very informative. Models are only one way to extract knowledge from data. There are many different types of models at different layers. Hence, models should be linked such that the output from one model can be the input to another model. The consensus goal was then extended to a broader definition that states: our goal is to develop data management solutions allowing for data integration aimed at improved extraction of knowledge from horizontal and vertical studies.
How can we achieve this goal? The problem is that we are currently not efficient in storing, searching and reusing data and models. One solution is to add meta-data to existing and new data and models. Many models do not describe specifically the entities that are modeled, limiting their broad utility. This concept has been termed "harmonization", notably as relating to work carried out by the Alliance for Cell Signaling. This approach was attempted but achieved only partial success. It is also useful to utilize modeling software that forces authors to heavily annotate their data when submitting papers for publication. Useful tools are being developed to facilitate the conversion of models and data into sharable formats automatically: e.g. JigCell ability to export models in ODEs and SBML, and tools to convert protein interaction networks and signaling pathways saved in text files into BioPAX format. Scientists are motivated to publish papers and will be willing to dedicate their time and effort to provide meta-data along with their studies. It was then agreed that there should be established data reporting protocols.
In those settings in which there is extensive use of Laboratory Information Management Systems (LIMS), these systems might be upgraded to become sharable. Sharing LIMS data presents several challenges such as privacy and interoperability. Thus, sophisticated tools are needed to help users to submit meta-data and provide data in formats that facilitate data exchange, improve search, and reusability of models and data.
Authors: QSPcol ConsortiumVersion: 20090223Meeting website:http://meetings.nigms.nih.gov/meetings/QSPcolWorkshop/
This Planning Document for QSPcol was produced in advance of the Sept 25-26 meeting. The meeting abstract and announcement is given below. During the meeting, five breakout sessions were convened:
Breakout Session 1: Horizontal systems integrationBreakout Session 2: Vertical systems integrationBreakout Session 3: Quantitative biology and pharmacologyBreakout Session 4: Education and trainingBreakout Session 5: Data management
Prior to the meeting, registered attendees were asked to provide written responses to the following areas
The responses were edited and assembled into this document. The meeting website contains report-out from the breakout chairs and scribes.
The Quantitative and Systems Pharmacology Workshop will provide state-of-the-art-knowledge and perspective about topics at the interface of systems biology and pharmacology to a highly diverse spectrum of researchers in academia, industry, and government. The question of where systems biology, modeling, and more quantitative measurements can be applied to pharmacology and drug discovery/action now and in the foreseeable future will be addressed. Accordingly, the talks will emphasize both conceptual information and significant research findings concerning the topic. The topic will be approached from the standpoint of both a horizontal integration (various networks in various cell systems) and a vertical integration (connections between pathways at different levels of integration, tissues, organs, etc.). Consideration of the state-of-the-art as well as future research needs in various areas will be made. The workshop will feature discussion sessions based on the presentations, throughout the two-day period. Meeting goals include highlighting where the state-of-the-art resides in relevant disciplines today, fostering integration of research efforts in these areas with others, identifying barriers (problems merging two disciplines), increasing collaboration between pharmacologists, clinicians, and systems biologists, and establishing pressing and long-term research needs (training, instrumentation, algorithms, etc) for advancement of the ability of systems biology to inform drug discovery and drug action.
NIH Program Staff: Michael Rogers (NIGMS), Sarah Dunsmore (NIGMS), Peter Lyster (NIGMS), Dick Okita (NIGMS), and Grace Peng (NIBIB)Meeting Co-Chairs: William Jusko (U Buffalo), Douglas Lauffenburger (MIT)
Systems integration is the act of assembling a composite system—computer models, in this case—from previously autonomous components used in specific contexts. Horizontal integration synthesizes a composite from components having the same spatial and/or temporal granularity.1 An example might be a cell pathway interaction model composed of various networks within and across different cells in various cell systems, but not including tissue or molecular dynamics. A clear statement of current and future uses to which an integrated system will be put is a precondition of systems integration for scientific research. A use statement typically begins with the current capabilities of the individual components followed by listing the expected capabilities of the integrated system.
For horizontal systems development and integration, what are the expected uses for these types of models? How should such horizontal modeling approaches be integrated? Are there envisioned uses for a horizontally integrated composite model that are distinct from uses envisioned for a vertically integrated model?
What kinds of experimental measurements are required to provide parameters and input to horizontal models?
In mammalian systems, the cell and tissue behaviors that emerge during experiments are the consequences of local mechanisms—local component interactions. As integrated system models become more realistic and useful, should we anticipate being able to say the same of them? If so, how do biological components (at multiple levels) interact with each other. Alternatively, are there emergent properties of Multiscale phenomena (subcells, cells, tissue, organs, organism) which are not explained by the sum total of local mechanisms, and how can this emergent behavior be modeled.
It seems unlikely that biological component interactions will be exclusively either horizontal or vertical. Should we therefore anticipate that issues of horizontal and vertical component integration would merge into a single integration issue?
How much of the modeling can be developed in a context-free manner, and what are the characteristics of the resulting implementation and software frameworks. Also, what are the approaches to handle the software needs of models that depend strongly on context.
Having a preliminary list of technologies that have succeeded or failed will help kick-start integration methodology. What data formats, formalisms, and standards have been successfully (or unsuccessfully) used for integration?
For software development, prioritize the usefulness of component interoperability versus component and system flexibility, adaptability, and reusability?
Because horizontally integrated systems consist of components having the same granularity, the data used to control, parameterize, and observe them will be semantically grounded at the same level. Doing so enables interoperability between components. Therefore, the format and protocol of data integration will rely on explicitly formulated ontologies and software and data exchange formats, e.g. XML or one of its derivatives like SBML. The same can be said of formalisms used to construct horizontally integrated systems. However, fixation on any given standard, formalism, or data format can result in component and/or system inflexibility, making it difficult to achieve new uses as the science advances.
Within the context of data integration, format, messaging, and standards, should we build and maintain competing and incommensurate standards, formats, and formalisms in order to promote and preserve technological agnosticism? What needs to be done to avoid fixating on any given standard, formalism, or data format? The risk of fixation may be greater for horizontally integrated systems than in vertically integrated systems because of the common semantic grounding due to a fixed spatial and temporal granularity.
Models of different organism components at the same granularity can have disparate data types and disparate models for how they interact with other components of the overall system. Is now the time to begin exploring methods for integrating these heterogeneous components into an overall system? An example of such a framework is the discrete event systems specification
The ability to therapeutically target the molecular signaling and transcriptional pathways that drive cancer will be enhanced by a more global understanding of how these pathways interconnect to create, through feedback and cross-talk mechanisms, the full signaling network that integrates all signals into a net outcome or phenotype (e.g., oncogenic transformations). The broader biomedical research community is searching for the underlying rules that govern signaling, while cancer researchers are simultaneously addressing through technology development the need to measure variations between tumor types, between tumors from different patients, and even within tumors through single cell measurements (e.g., integrated microfluidic-based assays). Using cancer models such as Bcr-Abl driven leukemic transformation, we are collecting high dimensionality phosphoprofiling signaling data focused specifically on subnetworks that involve the cross-talk between a small number of signaling modules. Simplification of the problem through subnetwork analysis, allows us to first focus on a more tractable scale, while retaining clinical relevance, with the hope that we can later expand the network diversity using the technologies and methodologies we are developing. Through iterative rounds of global measurements, perturbation of the signaling proteins involved (e.g., drug inhibition of nodes and mRNA knock down techniques), and measurement of resultant phenotype, we are building experimentally grounded theoretical descriptions of the oncogenic systems, along with establishing the basis of pharmacological interventions.
What are the uses of horizontally integrated system models? Such systems will be used as stand-alone software components to study specific networks, and they will also be used as components in larger horizontally and vertically integrated systems. Such uses depend on the driving biological problems. If the integrated model is intended to represent an organism's pharmacological response, for example, then the duration of the response cycle, the number of cycles considered, and required response granularity become determining aspects. With that in mind, model and component reuse, flexibility, and adaptability, become important and that feeds back into model and component design. Therefore, it should be relatively easy to reconfigure components to represent different mechanistic hypotheses or different aspects of a key attribute under different experimental conditions. It should also be relatively simple to accommodate additional aspects at the current level of granularity or alter usage and assumptions, without requiring significant component or system reengineering. Components should be constructed so that they can be adapted easily to function as components in different, integrated models.
Are there envisioned uses for a horizontally integrated composite model that are distinct from uses envisioned for a vertically integrated model?
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Vertical integration synthesizes a composite system using previously autonomous components that have different spatial and/or temporal granularities1. An example might be connecting cellular, tissues, organs, and whole body pathways. Clarity of uses and problems as well as application contexts are essential. Vertically integrated systems will need to be specified based on clear statements of usage, starting with the capabilities of the components, both in their original context and within the newly formed integrated system.
For vertical systems development and integration, what are the expected uses for these types of models? How should such vertical modeling approaches be integrated? Are there envisioned uses for a vertically integrated composite model that are distinct from uses envisioned for a horizontally integrated model?
How do we obtain data sets that that drive scientific understanding of how molecular variations and defects lead to changed behavior at the tissue/organ level? Vertical systems integration will require non-invasive time series experiments that track molecular level dynamics and related phenotypic behavior, e.g., imaging-related studies that relate quantitative phosphoproteomics to proliferation rate and migration rate assays. Problems include different characteristic
time scales (sec-min for proteins, hrs for migration, days for proliferation, weeks for
in vivo, months-years for patients) and
length scales (protein-cell-tissue), and thus the fact that, at least for the initial step, one will have to rely on pooling data from different experimental setups. Compounding the problem of assembling multiscale spatial images is that
in vivo studies often suffer from resolution limits.
What approach do we take to couple models at different scales? Different models have different methods of synchronism in time, different database structures, and heterogeneous input and output data types. What kinds of enabling technologies should be addressed to address these issues e.g., the Ptolemy II modeling framework?
The promise of multiscale modeling is to assess the functional impact of molecular perturbations across the scales on interest. This will allow cross-scale biomarker assessment and thus may accelerate target validation for drug discovery research. This in turn should facilitate the entry of targets into the drug development pipeline and thus help focus treatment and reduce development costs. Clinical endpoints include decision support and treatment impact studies.
What capabilities should we expect of such systems and their components? Once integrated, it is unlikely that a system model will remain static. New information will require revisions, especially if that new information falsifies the model in some way. To answer new questions, the use statement may need revision. We anticipate needing to deconstruct the vertically integrated system, revise or replace components, and then assemble a revised system to undergo the next round of validation challenge. How can we make accomplishing these tasks easier? From an algorithm and software development perspective, one needs to avoid having to go through constant, and most definitely, costly revisions. A modular design may be needed, e.g., connecting nodes between pathway modules necessitates translation of code modules that can communicate, which in turn requires shared practices or standards. This is highly non-trivial for a number of reasons, including technical. It could be pushed as a community effort if one could agree on common standards—here is where the NIH could provide leadership.
Like any other computational modeling, these platforms should lead to experimentally testable hypotheses, should facilitate data integration and eventually enable outcome prediction (clinical translation). The latter is the really challenging part, and it relies in large parts on the data integration.
Handling tool sets at the front end should be as user-friendly as possible. This is a GUI design and not a platform issue, as the underlying algorithm will likely be quite sophisticated. Any of this requires constant support—again an area where the NIH could provide leadership. Hiring talent away from industry for non-profit money is difficult enough. Long-term sustainability is important especially where peer-review is used primarily for funding.
How will system models be falsified and/or validated? This will require experimental data on all levels, and models should be trained on clinical data. Construction and evaluation (selection, validation, falsification, and execution) are two fundamental aspects of building any system model. Each comes with many specific methods designed to maximize efficiency and efficacy. Can all necessary methods be assembled into a coherent methodology? Are the families of methods of construction distinct from those of evaluation?
How do we assess generalizability of models at each spatio-temporal scale?
How will we use these models directly for planning, monitoring, and adjusting therapy optimally? What types of stochastic control techniques are employed, and why?
Having increasingly implicit integration methods for heterogeneous components will make vertical integration of multi-attribute, biomimetic systems more straightforward. The components will be models in their own right. Vertical integration will be made easier when multiple, heterogeneous models are able to operate simultaneously within a common simulation framework. Coupling within such a framework will require an automated means of system behavior evaluation during execution. To achieve this, data standards for data exchange as well as data characterization and classification are needed. Additionally, maintaining data quality through filtering techniques is critical.
Other issues: scalability; development of model sharing environments; develop of widely-used ontologies, IP issues; grid and high-performance computing access; and workflow design, storage and execution.
Can ontologies be used for integrating models at different levels (e.g. Gennari et al, 2008)? Are existing formats and standards (e.g. SBML, CellML) adequate for multiscale modeling? Having a preliminary list of technologies that have succeeded or failed will help kick-start integration methodology. What data formats, formalisms, and standards have been successfully (or unsuccessfully) used for integration?
We should expect that some details of vertical systems integration will be unique to the specific usage context, and that usage contexts will evolve and change. Consequently, we need to insure flexibility, adaptability, and reusability. For example, because it is infeasible to include an equivalent amount of detail in a sub-cellular model, as in a reasonably realistic organ model, we can anticipate that a vertically integrated system composed of both sub-cellular and organ sub-systems may require different patterns of messaging, data formats, formalisms, and standards for construction and evaluation. Does it follow that we need to avoid restricting to any given technology, including data formats, standards, or formalisms?
How do we characterizing usage and problem contexts for vertically integrated system and their components as a whole?
The In Silico Liver (ISL) exhibits elements of both vertical and horizontal integration. It uses an In Silico Hepatocyte (ISH, Yan et al. 2008) model as one of its sub-systems. The two separate aspects (use cases) are: 1)
in situ perfusion output fraction profiles for compounds studied the ISL and 2) uptake by cultured hepatocytes of compounds for the ISH.
Multi-formalism modeling tools can facilitate the synthesis of vertically integrated systems. For example, the Ptolemy II software framework allows one to assemble graphs where the nodes are software objects (or scripts) containing anything expressible in software (including network- or database-enabled callouts) and the edges are interactions between them. The semantics of the graph is added through various different "Directors," which specify what type of data goes across the edges and how the nodes interact (e.g. data-driven versus event-driven). Ptolemy's facility for specifying composite nodes, themselves governed by different "Directors," demonstrates its usefulness for the synthesis of hierarchical, multi-scale, and multi-formalism models. An example of such a model is provided by McPhillips et al. 2006, wherein different databases are assembled into a systemic workflow.
Systems biology provides a set of tools for specific tasks in drug discovery and development. The applications discussed in this break out group may begin at a stage when a target has been selected. This selection rationale is based on a varying foundation of mechanistic understanding. Examples of such rationales are:
Technical, legal and business considerations also factor into progressing a target. At the discovery stages of the drug development pipeline, there is considerable research on the chemistry of modifying the behavior of the target using a small molecule. Hidden in the pipeline is the work of optimizing the chemistry to make a compound that is stable, bioavailable, safe, manufactured on a large scale, and formulated for delivery. From a modeling standpoint this is a perfect example of a multiscale modeling problem. From a quantitative biology standpoint, the following table suggests a number of places where systems biology impacts the needs of the process.
Quantitative and systems pharmacology is the interface or boundary between drug discovery and development. Modeling and systems approaches are already being done on the development side (e.g., see 2008 meetings ACoP in Tucson and AAPS NCB in Toronto). An ideal work flow in quantitative systems pharmacology might be:
Defining the new combinations of competencies, masteries, habits of mind and interactive skills required by the future investigators who will successfully use systems approaches to understand diseases and address problems of drug development. Defining the pedagogies, curricula and activities that will constitute training for these new combinations.
Teaching (medical, pharmacy, as well as graduate students) thinking about biological systems (where vertical and horizontal integration are not constant) as distinct from engineering systems theories (where organization is fixed; and where the theory does not map well to biology) is a major challenge. The following are several reasons:
Do we need integrated courses both Pharmacy and Systems Biology grad students and MDs who would like to have a career in Systems Pharmacology and Therapeutics?
What further training in quantitative pharmacology is needed by those in Systems Biology to appreciate the intricacies of drug disposition and action?
Discuss the needs and barriers. Design educating and training programs for students in Quantitative and Systems Pharmacology:
Demand vs. supply analysis:
Defining the new combinations of competencies, masteries, habits of mind and interactive skills required by the future investigators who will successfully use systems approaches to address problems of drug development and drug interactions. Defining the pedagogies, curricula and activities that will constitute training for these new combinations.
One of the major limitations for a more widespread adoption in the scientific community, and in particular in the pharmaceutical industry and clinicians, is a great shortage of appropriately trained clinicians, pharmacists, and junior scientists that have sufficient expertise in this area and the associated techniques. The focus of the Group 4 breakout session should be to collect ideas and strategies on how to overcome this shortage. Over the last two decades, there has been a steady decline in academic programs and training sites that have traditionally provided or could provide education and training in quantitative pharmacology, predominantly programs in pharmaceutical sciences and biomedical engineering. This decline may be due to the reluctance and/or incapability of clinicians to adjust to these new approaches, and to understand them, to use them, and to teach them. The greatest challenge today may lie in creating new training sites by attracting and recruiting junior faculty and convincing academic administrations that investing in these junior positions is promising and important. Attracting external funding seems to be the greatest concern. Quantitative pharmacology as a translational science has for a long time not been a focus of federal funding, and many academic administrators as well as junior faculty still perceive it that way. It will be crucial for the success and development of quantitative pharmacology as a discipline to reverse this trend and establish a large and diverse number of educational programs and training sites that instruct a new generation of translational scientists with focus on quantitative and systems pharmacology.
Discuss how we can better prepare the next generation of biologists and clinicians for the increasing demands of quantitative and systems pharmacology. The session may be used to discuss what types of skills are needed as a quantitative and systems pharmacologist; how schools are training their students with those skills; what are the challenges during the training; how we can improve the training program. In addition, how much will the demands be from both academia and industry in the near term and long term; how many students are expected to graduate in the near future with the right skill set; is it necessary to expand our current education programs; if so, what are the challenges to expand the program (e.g. funding, teachers, attract potential students etc.)?
In this session, there should be a discussion on the need to expand current education program and if so, how big the expansion needs to be; to identify the biggest challenges for such training program; and to propose potential solutions for the challenges.
Environment: What may be required is a major research university with a comprehensive health affairs campus that includes a teaching hospital, translational research/clinical trials unit, availability of requisite support sciences (bioinformatics, biostatistics, genetics/genomics) and a strong relationship with the pharmaceutical industry and relevant federal agencies that can provide appropriately interactive training experiences for both graduate students and fellows would be required.
Data management involves three concepts: data formats for exchange; data messaging for communication; data storage. Special attributes of biomedical data are: heterogeneous data types; data acquisition pipeline; imperfect laboratory information management systems (LIMS); open and closed databases and tools.
Systems Pharmacology has particular challenges for data analysis and management because it involves the union of previously disparate informatics disciplines. First, the simulation and modeling aspects of systems biology are clearly the most relevant and applicable aspects of this work. But they require an informatics infrastructure that may be more diverse than other fields. Chemical informatics is critical in describing the structures and activities of small molecules. Bioinformatics focuses on the genes and protein products that are measured in the genome, the transcriptome and the proteome. Physiological modeling is important to connect the molecular scale to organs as an enabling tool for PK/PD. Finally, clinical informatics studies how clinical data can be organized and mined for significant phenotypic trends. At the same time, there are special, new data sources that may be specific to systems pharmacology. The goal of this breakout group is to answer the following questions:
Potential and challenges: Integration and scalability. Scalability (to clinically relevant levels), works towards multi-scale, multi-resolution modeling & simulation. Integration puts a focus on interoperability, ontologies, data storage, transmission, and formats. A question is: How are clinical trials that intend to address personalized medicine set up in the future if population-based assessments are deemed obsolete? Computational biology promises progress in the early stages of the pipeline and on diagnostics side first but it is also important to think early about how this possibly affects treatment, testing and clinical routine later on.
To discuss statistical and bioinformatic models for horizontal and vertical data integration, and to discuss approaches towards public access databases and parallelization of data interpretation.
Genome-enabled data sets as applied to pharmacogenomics systems biology include large data sets on multiple time scales. After treatment with a drug, multiple changes occur at the molecular level, including protein phosphorylation within seconds, proteomic profiling changes in localization occur in minutes, alteration of stored RNAs for translation within minutes, and transcriptional changes within hours. Serum markers and physiological changes can mirror any of these lower level molecular alterations, with added complications of organ-organ effects. It is now possible to 'profile' both low level molecular responses to a drug, and higher level reactions to these responses, resulting in a multi-scale physiome model of pharmacology.
Ideally, all levels and types of data from a specific drug response can be integrated and analyzed as unit, and cause/effect models able to predict drug responses evolved. Also, these large integrated data sets should be made public, so that the highly complex and innovative data analyses required for model development can be parallelized between many scientists worldwide.
This presents challenges for both data integration, databasing, and public access. Also, the statistical and bioinformatic tools required to evolve multi-scale physiome models of pharmacology are in their infancy. One approach that has emerged in the informatics community is developing workflows where tools and datasets are chained such that the output from one tool feeds into the input of another tool.
Data storage capacity and high-performance computation are two important components that should be addressed as well in this discussion. Current trend suggests that it is more economically feasible to centralize data storage as well as data processing. Google's success is the motivation behind this viewpoint.
More efforts should be put into data organization. The design of a global Systems Pharmacology data warehouse that would mine, organize, disseminate data in a cloud computing fashion is one potential major useful undertaking.
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