Maybe, but not today. Tomorrow morning is also quite unlikely. A single-shot approach has it's increasingly apparent limitations, but systems biology is not ready to deliver on the level we used to expect. Hyper-enthusiasm and over-investment in Systems Biology didn't work well for the Pharma and Biotech and investor disappointment only casts unnecessary and undeserved shadow over Systems Biology research. In fact there is no need to pitch Systems Approach against traditional "mechanistic", "hypothesis-driven", "candidate gene" and so on down the list of approaches. The order of the day for the strategists is finding the balance and appropriate packaging for both approaches in R&D. Please don't take this comment as discouraging. Just don't expect the revolution. The riot is over. Prepare to evolve.

Focus on systems biology is very important for both understanding and management. Conventional drug discovery and development has neither addressed the understanding of the disease process nor the management of disease. While it claims to have provided an extensive choice of drug targets and medications, it has prevented physicians and health care professionals learning the disease process and personalizing treatment protocols.Especially it has failed miserably in the management of all metabolic disorders and chronic diseases like diabetes, asthma, autoimmune disorders, cancer, cardiovascular disorders (hypertension). The advent of systems biology will not only help us understand the disease process but also help us arrive at effective disease management protocols/guidelines. Integrating Evidence based and translational medicine will further strengthen the approach. It will also set an excellent platform to identify and develop biomarkers for both diagnosis and monitoring disease management.

I think using Systems biology will be great aid for Drug Discovery and should definitely be the way forward. SB is a great tool that reduces complex biological systems into testable and a quantifiable form/model which will help us understand the complexity in a more intuitive (if possible) way.

If you look at the last decade or so, conventional drug discovery has been bootstrapping on great improvements in high throughput screening, and improvements in manufacturing, but the pharma industry realizes that all these approaches have now reached a plateau and something radical needs to be done. Bringing SB models into drug discovery will definitely improve the efficiency in finding new targets quickly. One important thing to note is that with talks about bringing open innovation and crowdsourcing approaches to drug discovery, I predict that SB will definitely play a key role as a game changer in drug discovery.

http://blogs.nature.com/spoonful/2012/02/open-innovation-drug-discovery-looks-to-the-masses-for-insight.html

Anyone enthused about the issues expressed above might like to look at our distance learning Masters course, Next Generation Drug Discovery, http://www.ngdd.ed.ac.uk, at the University of Edinburgh, UK. I would hate to alienate people by being thought to be spamming, but I can't resist since you convey the philosophy o f the course in a nutshell.

They are complimentary functions, not exclusive. The future of effective drug discovery is dependent upon Systems Biology or, as it should better be represented in this context, Systems Engineering.

Thanks for pointing this out Martin, this is a nice spotlight on an important issue. I agree with your comments and those of the previous commenters that systems biology is likely a useful tool for drug discovery. However, I think it is also important that we achieve a more uniform definition of Systems Biology, I still encounter too many people who say Systems Biology when they mean, for example, Bioinformatics. Personally, I like the method-based definition by Peter Kohl and others (Kohl & Noble. 2009. Mol. Syst. Biol. 5:292). A recent review by Winslow et al. (2012. Sci. Transl. Med. 4:158) also provides a nice overview, although both are mostly focused on the domain of cardiology (which is also my main interest).

It borders on deliberate amnesia to wave off 'traditional' drug discovery method as having 'miserably' failed. Because it has not. One should be careful in considering the typical reductionist approach employed in modern drug discovery from the standpoint of desired efficacy alone; in reality combining polypharmacological properties in the same compound in addition has the potential of at the same time increasing toxicity. The tenets of reductionism currently employed mostly and necessarily arose from need rather than the desire to defend a philosophical concept. As researchers working in the field of drug discovery, it is humbly to consider that there is a huge chasm between theory and practice, which chasm is can only be reduced with an increasing translation of theories into application. Unfortunately, that has continued to happen at a very slow pace. No doubt, traditional discovery has not solved all disease problems, but compared to all other discovery philosophies it by far has the most number of success stories. Especially with the rate at which most pharma industries are inculcating approaches such as structure-based discovery into their protocol which previously had about solely consisted in high-throughput screening of chemical libraries. Once the ideals of system biology have begun to be translated into actionable applications, the 'traditional' methods of discovery can then be upgraded.

Maybe, but not today. Tomorrow morning is also quite unlikely. A single-shot approach has it's increasingly apparent limitations, but systems biology is not ready to deliver on the level we used to expect. Hyper-enthusiasm and over-investment in Systems Biology didn't work well for the Pharma and Biotech and investor disappointment only casts unnecessary and undeserved shadow over Systems Biology research. In fact there is no need to pitch Systems Approach against traditional "mechanistic", "hypothesis-driven", "candidate gene" and so on down the list of approaches. The order of the day for the strategists is finding the balance and appropriate packaging for both approaches in R&D. Please don't take this comment as discouraging. Just don't expect the revolution. The riot is over. Prepare to evolve.

Systems Biology will help us to understand what is going wrong in particular diseases and with particular patients, but you will still need chemists designing (or engineering) compounds and synthesizing them in order to repair dysfunctional networks. I think there is tremendous potential for Systems Biology to provide new targets for drug discovery and perhaps even sets of targets, which may indeed lead to a brave new world of multiple target treatments. However, there is a long road ahead and the fundamental problem is that we still do not understand the molecular mechanisms of important diseases. Chemists have done very well in designing molecules to interact with specific targets while minimizing side effects. My personal hope is that Systems Biology will help us identify highly valuable targets so that Medicinal Chemistry can do its job.

I have never known a single target-based drug discovery person who did not realize that even highly targeted molecules work on systems and that successful therapy depended on this. The question is not whether systems biology is important, but how do you use it to discover novel therapies. Indeed, the best targeted approach comes from knowing how a system works and where good interventions may lay.This sometimes teaches that one needs to target several things at once, or hit a target in a novel way. I am unclear how one applies systems biology without having it ultimately lead to specific ways to intervene.

Being a systems biologist myself in pharmaceutical industry and academia for the past 10 years or so, it is my passion to promote systems biology as the future direction for both disease mechanistic studies and drug discovery (see our recent review articles "Functional genomics- and network-driven systems biology approaches for pharmacogenomics and toxicogenomics", Current Drug Metabolism 13(7):952-67, 2012; Systems Biology Approaches and Applications in Obesity, Diabetes, and Cardiovascular Diseases. Current Cardiovascular Risk Report. Advanced Online Publication (DOI) 10.1007/s12170-012-0280-y.)

I think we have already made great progress in understanding disease pathogenesis using systems biology approaches. However, I agree with many colleagues that we are just at the beginning of a long journey in the drug discovery domain. Increased awareness of systems biology methodolgoies, coordinated efforts, and patience are critical for our future success. In my opinion, systems biology should and will coexist with traditional approaches to expedite drug discovery processes.

I think systems biology will be very useful for drug discovery. A traditional way of developing drug was very successful for some diseases. However, for the complex chronic diseases, systems biology approaches needs to be introduced. It is quite clear nowadays that the effects of shooting a single target given a very complex biological network might be compromised by many other pathways. Unless we can identify the essential ‘hub’ components of the complex biological network, we cannot only deal with one target. Furthermore, to predict novel drug targets by systems biological approaches is also essential.

Starting of with a small disclaimer: I am a total SB enthusiasts.

Nevertheless I would like to play the 'advocatus diaboli' here.

Working in drug discovery at the moment my sincere concern is the omission of viable drug candidates due to models that are reductionist in another way and thus wrong in prediction.

In any SB study the system models have to be confined to a certain amount of agents - a certain level of complexity. Knowing the problems that arise from complexity itself and from the the attempt of depicting it in a model this is approach is of course without alternative. Nevertheless it is reductionism in the first place.

Also there is the issue of the used constants for agents in the system. Even though many groups work on means to validate the information on single building brick properties from non-SB studies, one has to face that most data is rather contextual and might not be applicable to specific model conditions. Including those constants in a model anyway might change the properties - not completely but slightly.

Learning from systems theory, two system composed of "almost" the same agents can elicit completely different behavior.

Considering this aspects it is safe to say that this could lead to discrepancies in emergent behavior or input-output properties between model and actual system. This might particularly be true at the system's extreme points that are of most interest for both: pathology and adverse drug action.

This could mean, that models despite depicting complex behavior, might not depict the system they aimed for, but another variant.

If a new compound is now found to result in system deregulation for the model it is most likely not to be investigated further for effects in vivo.

My question in this context is now:

Are we even losing more fit drug candidates? Or to put it in another way: Is SB really selecting better candidates for the complex system than hypothesis-driven approaches?

BTW: This argument is not new. It was introduced for animal studies to point out their limitation in prediction capacity for the human system.

Chris

In my opinion, Systems biology approaches can compliment the Drug discovery in finding the target with taking proper consideration of individual bio-system. Also, it should be noted that we can develop the new ways to discover a novel drugs with ignoring the complexity of human system. But this is long way to go in adequate marriage of these two fields (they are always related).

My opinion is something different,system biology approaches helps us to solve drug discovery problem by the use of biomolecular networks.but it is not wise to say it will overtake drug discovey or it is future of drug discovery.

Actually, I differ in my answer to this question from several preceding opinions, based on facts. There are already several signaling pathways blockers that were rationally designed as anti-cancer agents; several worked quite well. Complex Systems Biology can make a very big difference to improving the rational treatment of clinical trial patients. Of course it needs to be combined with a molecular biology/molecular structure approach to be successful, just as in the case of the rational design of anti-cancer drugs referred to in the attached concise article and references cited therein. (In part, this answer is in agreement with the answer given by Pryiam, but with much more detail of why, and especially how.)

v. Editorial J Clinic Trials 2012, 2: e103

doi: 10.4172/2167-0870.1000e103

Cancer Clinical Trials Optimization and Pharmacogenomics

I don't think so. Systems biology look for clues by mass screening which is more objective.However, many important discoveries are based on the assumption that, not out of thin air, but based on the results of the various studies already made.

Systems biology will certainly move ahead of drug discovery as information gained through systems biology approach will pave the way for New drug discovery.I strongly feel that drug discover programs will demand optimistic look towards SYSTEM BIOLOGY.........Prashanta Kumar Pal, Institute of Biomedical Education and Research, Mangalayatan University, Aligarh, Uttar Pradesh, India. (Faculty for: Drug Disscovery, Nanobiotechnology and Systems Biology).

The two challenges facing systems biology are (1) lack of temporal data on complex diseases; (2) high-throughput technology for measuring multi-omics from the same biological sample is yet to exist. Despite these challenges, systems biology at present still provides a more holistic view towards the 'efficacy vs toxicity' dilemma than conventional molecular biology. That's because the latter assumes a linear view towards cellular pathways rather than a complex adaptive system which is non-linear. For the drug developers, when they put on the systems biology thinking cap, they will find that the 'efficacy vs toxicity' dilemma is steeper than expected. They will wish that the complex systems physicists can give them a crash course on complex systems theory 101. Your brain has to evolve fast to stay in this game.

Systems biology has a definite future in drug discovery. It helps with rapid screening for potential drug targets and find co-targets to combat drug resistance. While systems biology is relatively a young field, further development relies on selecting robust HTP platforms (which can accurately measure different biological responses) and our abilities to integrate data (from multiple platforms) and extract usable knowledge. It may seem very complex, but a considerable progress has already been made.

The obvious issues are,

There are multiple types of biological responses (do we have a complete list and how many could we measure in a HTP format?) and different biological response are often interconnected (how are we going to navigate through these interconnections?).

How do we account for spatio-temporal relationships (in terms of dynamics of biological responses and their interactions).

How to account for other variables such as cell/species type, microenvironment etc.

Regardless, with careful planning and good investments, if not all, many issues could be addressed. Good leadership and team effort are crucial, but it takes time to get organized and also requires patience.

Systems Biology has to overtake drug discovery. Screening compounds on individual targets and application of targeted therapy based on this elementaristic approach has basically disappointing results in clinics, while huge money has been wasted on this kind of research (very advantageous for pharmaceutic companies) for decades.

I don't see that pharma has a choice in the matter. I see a few different definitions of what systems biology "is" here (understanding the topology of the transcriptional network, defining the "parts list" of the system, understanding the temporal relationships). What they all have in common is an attempt to go beyond focusing on a single entity in the complex system that is biology. Is there an alternative to developing a quantitative, complete, dynamical understanding of the components in a relevant model? I don't see it. Without that, you're left with developing drugs based on single readouts and gut feelings. How well has that worked in the past?

Some clarifications, performing high-throughput screening just for candidate selection is not systems biology, because the data here is filtered to identify top candidates. Systems biology is more of a descriptive science, where all hits from a unbiased screening procedure are considered together in the analysis of data, however the major problem that affect the translation of results is due to the methods used for data interpretation, whether using GO annotations or PPI or transcriptional network or combinations of such data to connect the dots. Because such interpretations are only projections and would require further validations. However adding more layers of data from several different platforms (and incorporating in vitro and in vivo experiments) could increase the resolution on our understanding about the system wide responses, that would be something very powerful to enhance our knowledge about drug functions. Currently, even with targeted drugs we don't have a complete understanding on its mechanism of action, such as what are it's off-targets, what the biological barriers to it's action). Once a thoroughly tested workflow (screening-data interpretation-validation-verification) is optimized, implementing such methods will then have a powerful role in drug discovery process. While it may appear cost prohibitive now, in the long-term such costs could eventually drop down. I am sure we will see more utilities for systems biology in drug discover in a decade from now.

Definitely system biology will do it, but no today. Currently, Its in its infancy. With efforts from researches around the world, the field is developing. Personally speaking i have a great hope with the Encode project, it would be really great if system biology integrates it. The day when we have sufficient knowledge of protein-protein interaction and pathways, will definitely have shorter drug discovery period and also less toxic drug will come to market. We just need to keep pushing it.

Many sage words in these posts. I think some clarity around the term 'system' might also help (at least to me). Do we mean screening a vast patient population, an intact organism/individual, organ, tissue, cell or other? Each could be considered as a system from a particular perspective. I'd also respectfully challenge the term ‘Traditional’ as used above. Personally I consider the system-based drug discovery model to be more traditional than the target-centric approach facilitated by the advent of molecular biology/pharmacology. Phenotype-to-target is arguably how many of the big blockbusters were identified and almost never for the indications they were originally intended

It is of course a gross over simplification to consider the issue in such terms but I wholeheartedly agree with others here that it is needlessly counterproductive to think about one approach being better or overtaking the other. The reality is that greater disease understanding and the identification of agents that can modify those disease states will be best served by combining both approaches. The whole being more than the sum of the parts, etc., etc. Obviously that’s hard to do (otherwise we would all be super smart, healthy and rich) but I’d argue that organisational, logistical and funding challenges are more readily solved than the scientific complexities of disease.

“Can Systems Biology Overtake Drug Discovery?” I hope not but only because I don’t agree that there should be a race.

To Jeff Jerman: You are right. The correction is that "systemic thinking would overtake the reductionist thinking' but the methods for drug discovery should be used in combination.

Reply:

Frank Gibbons · 26.27 · 190.34 · from AstraZenecaIs said: "Is there an alternative to developing a quantitative, complete, dynamical understanding of the components in a relevant model? I don't see it."

Yes, there is such an alternative, but it does not exclude--it includes understanding the components as well; part of the problems is that several such components may not be, and are not, known. Without having complete knowledge the components only approach does fail. As I posted here, and published elsewhere over 40 years, just such an approach that has been developed by combining a complex systems biology approach with understanding quantitatively highly-complex system dynamics and molecular biology data megabases covering components and more. In its applications to microorganisms it has been singularly successful. The fact that one "does not see it", does not mean that the approach required does not already exist! The purpose here at ResearchGate is precisely that --to exchange information because no single individual can know it all ... If Frank Gibbons followed ,for example, my site and publications posted at ResearchGate he would find several of the answers that he said "does not see", that is, if I understood him correctly in the above posting ?

Here's an interesting preface from a recently published book on a related topic:

http://www.kressworks.org/page/Systems%20Engineering%20Is%20Required

With a personal background of engineering, biochemistry and cell biology, I see little likelihood of any significant drug breakthroughs without the recognition of the importance of environment that was addressed by Denis Noble in “Genes and causation, 2008”, regardless if systems biology (SB) overtakes drug development or not. While many of us fundamentally recognize that external dietary and other factors play a major role in regulating cellular environment, we should also be aware that it is against this presently dynamic mix of questionable metabolites that all drugs are tested. Does this make sense?

Efforts to control this environment through a basic fundamental dietary input and the establishment of a set of measurable metabolic dietary boundary conditions could go a long way in creating a level field against which to evaluate drug and other metabolic inputs. Yes, I recognize that just the creation of a task force to determine the factors and inputs that might create this optimum cell environment would in itself be controversial over and above the decisions regarding the factors to be controlled. Yet, I remain convinced that only by facing this challenge with an open and altruistic mind will cancer be defeated regardless of SB or other approaches.

Systems Biology currently complements in the drug development and drug discovery process. So for now, Systems Biology cannot overtake drug discovery or has not overtaken drug discovery. However, in the future, advancements in Systems Biology can possibly have certain edge over drug discovery.

I agree with Ravi’s ‘Systems biology is more of a descriptive science’. It is relatively new branch of bioinformatics study. The advantage of this study is able to process a huge amount of information. However, I believe it was over evaluated and over invested. I know some researchers working on this field and realized some of them even do not know what they are doing. You can never expect them to find a novel drug based on their analysis of the molecular-net. The most important part of new drug development is based on the in vivo function. The system biology can be used for the first step screening (using it’s powerful analysis function). It can never replace the bench works.

Really good to read so much descriptive importance of System biology and drug designing, all the above points made by all scientists/Professors are really very good and full of knowledge.

Systems Biology can be an antidote to help people to abandon the simplistic view of 'magic-bullet' hitting the target (i.e. a receptor) that starts a deterministic chain crossing different scales and arriving to the macroscopic effect.

This will probably help the biomedical scientists to re-gain an appreciation of what does it mean to work at different scales and try and take into consideration meosscopic end-points and to abandon some false myths like 'try to select only very specific binders and discard weak binders to many tragets' when in practice (see for example Hopkins and Csermely works on network pharmacology) in many istances they are the most promising ones. Thus Systems Biology can be helpful if it is not intended as the 'definitive list of all the interactions' but a step forward a sort of biological statistical mechanics.

Systems Biology could aid drug discovery, but may not overtake or bypass traditional drug discovery process. The power of Systems Biology relies on the strength of biological networks, which is also a weakness because, biological networks are dynamic, and interpretations are often based on querying static networks represented in databases. Physiological networks are complex dynamic entities, subject to influence by multitude factors, and on top of that pathological perturbations could dramatically alter the topology of networks, so how on earth are we even going to capture such dynamic changes? The more we learn, and the more we add to the knowledge of network dynamics, and our ability to close the knowledge gaps on network dynamics will dictate the futuristic possibility for systems biology to overtake drug discovery.

I think a better question is "How Systems Biology can assist drug discovery?".

Or, to expand on Chengkun's comment, how might systems biology approaches be used to complement existing drug discovery tools and platforms? People need to realize the limitations of a systems approach. There is certainly a need for ways by which to integrate and process high throughput data in a logical fashion, but also need to realize the output is simply predictive, and often quite sparse. So, it offers a general means of integrating a large amount of data, but must be complemented with appropriate follow-up experiments to provide functional validation.

Radoslav, who said a network is a "fixed" entity? Unfortunately, due to simplification, people just look at them like they are static. Think of a biological network as a fluid template which may comprise any previously observed interaction or relationship, regardless of whether or not the template is complete, and whether or not the interactions occur in the biological state under examination (health, disease, diurnal effects, aging, response to therapy or drug Tx, etc.), in essence, a compilation of possibilities. It simply provides a foundation upon which to make some logical sense of multidimensional data, without having to be either complete or fixed. Acquisition of data only reveals small portions of a "complete" biological network at any one time anyway, and that portion will in turn be limited by inherent biases/limitations of applied techniques. Since we don't know all possible interactions within a particular biological system, and likely never will, it is always an approximation. However, the more information you include, the more likely it is that your observed "snapshot" of an overall network will allow you to in turn generate reasonable hypotheses that you can then set out to validate experimentally. The problem is that the more information you embed in the initial network, such as edge weighting and probabilistic or situation-dependent interactions, the more difficult it becomes to comprehend and the less likely it will be that anyone will attempt to make use of it. In other words, keep it simple, and use a network-based approach as a tool for guidance in developing experiments for validation, rather than assuming that the network itself provides that validation, as we all know it doesn't.

i believe both work hand-in-hand rather than one over-take another. Why kills each other when you could use both expertise =)

Okay Radoslav, I see where you're coming from, but I don't think biologists are ready yet to incorporate space-time in their views of biology. It's hard enough getting them to even consider thermodynamics in the context of modeling biological function.

Who knows, Maybe Rodoslav can pioneer the field of drug discovery using reverse time and relativity =)