QSP

Use of Quantitative Systems Pharmacology in Pharmaceutical R&D

What :

Quantitative systems pharmacology (QSP) modeling and simulation is integration of two disciplines that have been increasingly useful in pharmaceutical R&D; combination of Systems Biology and Quantitative Pharmacology.

  • Systems Biology is the field of biomedical research including those between genes and biologically active molecules to develop models of these systems that are usually qualitative in nature.
  • Quantitative Pharmacology is the field of biomedical research that seeks to use computer aided modeling and simulation to increase our understanding of the pharmacokinetics  and pharmacodynamics of drugs, and to aid in the design of preclinical and clinical experiments.
  • QSP is a rapidly growing discipline that incorporates computational modeling and experimental methods to investigate drug action.
Why:

Traditional pharmacokinetics (PK) has been a major challenge in classical drug discovery: many compounds failed because they had unfavorable PK half-lives or distribution in humans. The traditional PK rarely takes into account the physiology and biology of the human body. However, physiologically based PK (PBPK) is built mainly from drug-independent system information. This has been attributed to a greater connectivity to in vitroin vivo extrapolation (IVIVE) techniques for predicting drug absorption, distribution, metabolism, and excretion (ADME) and their variability in humans. PBPK–IVIVE linked models have repeatedly shown their value in guiding decisions when predicting the effects of intrinsic and extrinsic factors on PK of drugs. Therefore, it will be a better strategy in extending the success of PBPK–IVIVE to pharmacodynamics and drug safety.

How:

QSP combines both computational and experimental methods to validate and apply new pharmacological concepts to the development and use of small molecule and biologic drugs. QPS would maximize therapeutic benefit and minimize toxicity and implement a precision medicine.  QSP models will be critical to increasing the probability of success will be in the target identification stage, the transition from pre-clinical to first in man studies, the transition from healthy volunteer to patient studies, and the transition from adult to pediatric. Modeling and simulation to guide the design of experiments intended to test hypotheses. In addition to the utility in translation between experimental models, QSP allows prediction of the effects of multiple therapeutic interventions in combination. QSP can provide the frame work in which to evaluate the potential combination medications prior to testing in the clinic, by providing a fundamental systems and quantitative understanding of how these different mechanisms will interact. Reported work in the literature described the use of QSP modeling and simulation to facilitate biomedical research and pharmaceutical R&D. Most of these publications have been focused on PK, since the processes that govern drug absorption, distribution, metabolism, and excretion are better established compared to those that govern disease biology and PD. In a report author demonstrated that the use of the physiologically based PK (PBPK) models for prediction of PK in children prior to the conduct of the first pediatric clinical study.. There are software packages available that can be used to develop and run models and it will allow prediction of in vivo drug PK based on the in vitro properties of the molecule.  QSP models that predict both PK and PD are much more complex, and tend to be disease area specific. In an another published work, the QSP model of cognitive deficit in schizophrenia and was able to simulate the enhancement of cognition with clozapine and risperidone, as well as the worsening of cognition with gama-aminobutyric acid modulators lorazepam and flumazenil. Published work can be found in scientific journals like:

CPT: Pharmacometrics & Systems Pharmacology

http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2163-8306

Bioinformatics

http://bioinformatics.oxfordjournals.org/content/25/19/2466.short

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