The pharmaceutical industry is huge and has a direct impact on millions of patients across the world and the industry deals with a large amount of crucial data that needs to be organized, processed, and worked on effectively for efficient patient care and valuable drug development.
The Healthcare Industry is facing many challenges including high failure rates at the time of new drug development programs. Quantitative Systems Pharmacology can help to a great extend to make the decision-making process on various stages easier. The success of any clinical trial or study and research depends on efficient management of huge healthcare data.
Quantitative Systems Pharmacology
Quantitative Systems Pharmacology is an integrative approach to knowledge from varied disciplines such as drug pharmacology, systems biology, physiology, biochemistry, and mathematics. Quantitative Systems Pharmacology was formally defined and endorsed as a discipline in the NIH White Paper (Sorger et al., 2011) in 2011.
Quantitative Systems Pharmacology is a field of discipline that comes under biomedical research that makes use of mathematical computer models to study, disease processes, biological systems, drugs, and pharmacology. Quantitative systems pharmacology is also known as QSP. It is also a sub-category of Pharmacometrics.
Clinical trials involved organized and detailed studies of drugs and inefficiency in any of the processes can result in failure resulting in huge losses. Drug discoveries are mainly based on the qualitative link between the target and clinical outcomes. These studies are mostly supported by using animal models that sometimes have limited translation. Qualitative Systems Pharmacology helps by providing the missing quantitative link between target modulation and clinical results by helping in the selection of the right target, estimating the optimal target engagement while identifying the patient group that would benefit most from the therapy and assisting in designing the best trial.
Benefits of Quantitative Systems Pharmacology
There are many benefits derived out of QsP such as overcoming challenges of efficiency, overcoming challenges in productivity and R&D, allowing researchers to evaluate multiple hypotheses in-silico that may require experimental evaluation, reducing the cost of R&D, mitigating the risks associated with uncertainties and gaps in knowledge, while bringing new drugs to the market; optimization of clinical dose, optimization of schedule and provision of mechanistic explanations for clinical data.
However, there are some technical challenges of Quantitative Systems Pharmacology. A major challenge is the lack of an accepted standard modeling tool to facilitate sharing of models between researchers and experts limits the widespread use of QSP. It is crucial for the tool to have permission to evaluate experimental scenarios of interest in a computational environment that is flexible. This is necessary for conducting an efficient high-throughput simulation.
In the research paper, “Using quantitative systems pharmacology for novel drug discovery”, Violeta I Pérez-Nueno says, “The use of network biology produces valuable information such as new indications for approved drugs, drug-drug interactions, proteins–drug side effects and pathways–gene associations. However, we are still far from the aim of QSP, both because of the huge effort needed to model precisely biological network models and the limited accuracy that we are able to reach with those. Hence, moving from one molecule to one target to give one therapeutic effect to the big systems-based picture seems obvious moving forward although whether our current tools are sufficient for such a step is still under debate.”
QSP has a great potential in the fields of biomedical research and in the decision-making in pharmaceutical R&D. It is going to be used extensively in the future in all healthcare organizations.