Next-Generation Model-Based Drug Discovery and Development: Quantitative and Systems Pharmacology

Allerheiligen, Sandra R. B.

doi:10.1038/clpt.2010.81

Cited by 50 publications

(42 citation statements)

References 7 publications

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“…In general, the ability to make predictions of future experiments and results by the integrative concept of our translational approach is of importance in drug development, as drug failure that emerges in the late phases of clinical development is often due to insufficient efficacy. 37 The predicted population simulations together with the identified sources of PK variability could support among others evaluation steps in clinical development, where the challenge is to make decisions about project termination as early as possible. 33, 37 …”

Section: Discussionmentioning

confidence: 96%

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Translational learning from clinical studies predicts drug pharmacokinetics across patient populations

et al. 2017

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Early indication of late-stage failure of novel candidate drugs could be facilitated by continuous integration, assessment, and transfer of knowledge acquired along pharmaceutical development programs. We here present a translational systems pharmacology workflow that combines drug cocktail probing in a specifically designed clinical study, physiologically based pharmacokinetic modeling, and Bayesian statistics to identify and transfer (patho-)physiological and drug-specific knowledge across distinct patient populations. Our work builds on two clinical investigations, one with 103 healthy volunteers and one with 79 diseased patients from which we systematically derived physiological information from pharmacokinetic data for a reference probe drug (midazolam) at the single-patient level. Taking into account the acquired knowledge describing (patho-)physiological alterations in the patient cohort allowed the successful prediction of the population pharmacokinetics of a second, candidate probe drug (torsemide) in the patient population. In addition, we identified significant relations of the acquired physiological processes to patient metadata from liver biopsies. The presented prototypical systems pharmacology approach is a proof of concept for model-based translation across different stages of pharmaceutical development programs. Applied consistently, it has the potential to systematically improve predictivity of pharmacokinetic simulations by incorporating the results of clinical trials and translating them to subsequent studies.

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Section: Discussionmentioning

confidence: 96%

“…37 The predicted population simulations together with the identified sources of PK variability could support among others evaluation steps in clinical development, where the challenge is to make decisions about project termination as early as possible. 33, 37 …”

Section: Discussionmentioning

confidence: 96%

Translational learning from clinical studies predicts drug pharmacokinetics across patient populations

et al. 2017

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“…There has been a near constant flow of new terms introduced into the literature1 in an attempt to capture this phenomenon: “MBDD,”2 “model‐facilitated” or “model‐informed drug development,”3 “Quantitative and Systems Pharmacology,”4 and “pharmacometrics.” Large pharmaceutical companies have begun to review, quantify, and report the successes derived from the adoption of a model‐based strategy, providing a thorough description of its implementation and impact 5, 6, 7. The US Food and Drug Administration (FDA) recently utilized mechanistic model‐based methodologies to design a postmarketing clinical trial8; serving as a clear demonstration of the increasing confidence in and adoption of model‐based techniques in pharmacology.…”

Section: Agent‐based Models: Introduction and Applicationsmentioning

confidence: 99%

Agent-Based Modeling in Systems Pharmacology

Cosgrove

Butler

Alden

et al. 2015

CPT Pharmacometrics Syst. Pharmacol.

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Modeling and simulation (M&S) techniques provide a platform for knowledge integration and hypothesis testing to gain insights into biological systems that would not be possible a priori. Agent‐based modeling (ABM) is an M&S technique that focuses on describing individual components rather than homogenous populations. This tutorial introduces ABM to systems pharmacologists, using relevant case studies to highlight how ABM‐specific strengths have yielded success in the area of preclinical mechanistic modeling.

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“…[1][2][3] QSP models in the drug discovery and development process have been utilized for increased confidence in rationale for early development targets, preclinical to clinical translation, and predictions of clinical response to novel therapeutics. To be fit for this purpose, these models must include sufficient biological scope and mechanistic detail to link pathway modulation to overall system response.…”

Section: Study Highlightsmentioning

confidence: 99%

Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models

Rieger

Musante

2015

Preprint

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Quantitative systems pharmacology models mechanistically describe a biological system and the effect of drug treatment on system behavior. Because these models rarely are identifiable from the available data, the uncertainty in physiological parameters may be sampled to create alternative parameterizations of the model, sometimes termed "virtual patients." In order to reproduce the statistics of a clinical population, virtual patients are often weighted to form a virtual population that reflects the baseline characteristics of the clinical cohort. Here we introduce a novel technique to efficiently generate virtual patients and, from this ensemble, demonstrate how to select a virtual population that matches the observed data without the need for weighting. This approach improves confidence in model predictions by mitigating the risk that spurious virtual patients become overrepresented in virtual populations. CPT Pharmacometrics Syst. Pharmacol. Study HighlightsWHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? þ Parameter uncertainty in quantitative systems pharmacology models may be explored by the creation of virtual patients, which are typically weighted to form virtual populations to match clinical populations. Several algorithms designed to weight virtual patients have previously been published.• WHAT QUESTION DID THIS STUDY ADDRESS? þ Given that the parameters of a systems model are underconstrained, can we explore this uncertainty to efficiently generate physiologically reasonable patients and construct virtual populations where weighting is not necessary? • WHAT THIS STUDY ADDS TO OUR KNOWLEDGE þ This study outlines a methodology that improves on previous methods for efficiently generating and selecting virtual patients to match clinical population-level statistics. The final fitted populations will closely match empirical data, with all virtual patients weighted equally, which avoids the potential for overweighting certain solutions and skewing simulation results found in some previous algorithms.• HOW THIS MIGHT CHANGE CLINICAL PHARMACOLOGY AND THERAPEUTICS þ Generation of realistic virtual populations, and a deeper exploration of parameter uncertainty, should lead to better confidence in the predictions and better quantification of uncertainty of systems pharmacology models, particularly in the context of clinical trial simulations and analysis.Quantitative Systems Pharmacology (QSP) models are an effective approach for gaining mechanistic insight into the complex dynamics of biological systems in response to drug treatment.1-3 QSP models in the drug discovery and development process have been utilized for increased confidence in rationale for early development targets, preclinical to clinical translation, and predictions of clinical response to novel therapeutics. To be fit for this purpose, these models must include sufficient biological scope and mechanistic detail to link pathway modulation to overall system response. [4][5][6][7][8] Due to the complexity of the biology, the iterative model-building pro...

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Next-Generation Model-Based Drug Discovery and Development: Quantitative and Systems Pharmacology

Cited by 50 publications

References 7 publications

Translational learning from clinical studies predicts drug pharmacokinetics across patient populations

Translational learning from clinical studies predicts drug pharmacokinetics across patient populations

Agent-Based Modeling in Systems Pharmacology

Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models

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