Structural identifiability and indistinguishability of compartmental models

Jw, Yates; Rd, Jones; Walker, Mike; Sy, Cheung

doi:10.1517/17425250902773426

Cited by 23 publications

(18 citation statements)

References 34 publications

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“…While identifiability is the property of a certain parameterized model, a related notion called distinguishability addresses the problem whether two or more parameterized models (with the same or with different structure) can produce the same output for any allowed input [46-48]. The literature about identifiability and distinguishability of biological and chemical system models is relatively wide: Compartmental systems (that form a special subclass of general mass-action networks) are studied in [38,49,50]. The authors treat general nonlinear CRNs in [51,52] and [53] where it is shown that for thermodynamically meaningful models, nonlinearity reduces the chance of indistinguishability compared to the linear case [54].…”

Section: Introductionmentioning

confidence: 99%

Inference of complex biological networks: distinguishability issues and optimization-based solutions

2011

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BackgroundThe inference of biological networks from high-throughput data has received huge attention during the last decade and can be considered an important problem class in systems biology. However, it has been recognized that reliable network inference remains an unsolved problem. Most authors have identified lack of data and deficiencies in the inference algorithms as the main reasons for this situation.ResultsWe claim that another major difficulty for solving these inference problems is the frequent lack of uniqueness of many of these networks, especially when prior assumptions have not been taken properly into account. Our contributions aid the distinguishability analysis of chemical reaction network (CRN) models with mass action dynamics. The novel methods are based on linear programming (LP), therefore they allow the efficient analysis of CRNs containing several hundred complexes and reactions. Using these new tools and also previously published ones to obtain the network structure of biological systems from the literature, we find that, often, a unique topology cannot be determined, even if the structure of the corresponding mathematical model is assumed to be known and all dynamical variables are measurable. In other words, certain mechanisms may remain undetected (or they are falsely detected) while the inferred model is fully consistent with the measured data. It is also shown that sparsity enforcing approaches for determining 'true' reaction structures are generally not enough without additional prior information.ConclusionsThe inference of biological networks can be an extremely challenging problem even in the utopian case of perfect experimental information. Unfortunately, the practical situation is often more complex than that, since the measurements are typically incomplete, noisy and sometimes dynamically not rich enough, introducing further obstacles to the structure/parameter estimation process. In this paper, we show how the structural uniqueness and identifiability of the models can be guaranteed by carefully adding extra constraints, and that these important properties can be checked through appropriate computation methods.

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Section: Introductionmentioning

confidence: 99%

Inference of complex biological networks: distinguishability issues and optimization-based solutions

2011

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“…Because of their increased complexity, the majority of the system-specific parameters in PBPK models are fixed to physiological values to enable identification of the drug-relevant parameters of interest. [6][7][8] PBPK modelling can be viewed as a modelling platform and has found a useful niche for modelling antibody drugs. 9 3 | POPULATION PK/PD Population PK/PD is the most widely used approach for modelling PK/PD data from human studies.…”

Section: Pharmacokinetic and Pharmacodynamic Modellingmentioning

confidence: 99%

Machine learning in pharmacometrics: Opportunities and challenges

McComb

Bies

Ramanathan

2021

Brit J Clinical Pharma

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The explosive growth in medical devices, imaging and diagnostics, computing, and communication and information technologies in drug development and healthcare has created an ever-expanding data landscape that the pharmacometrics (PMX) research community must now traverse. The tools of machine learning (ML) have emerged as a powerful computational approach in other data-rich disciplines but its effective utilization in the pharmaceutical sciences and PMX modelling is in its infancy. ML-based methods can complement PMX modelling by enabling the information in diverse sources of big data, e.g. population-based public databases and disease-specific clinical registries, to be harnessed because they are capable of efficiently identifying salient variables associated with outcomes and delineating their interdependencies. ML algorithms are computationally efficient, have strong predictive capabilities and can enable learning in the big data setting. ML algorithms can be viewed as providing a computational bridge from big data to complement PMX modelling. This review provides an overview of the strengths and weaknesses of ML approaches vis-à-vis population methods, assesses current research into ML applications in the pharmaceutical sciences and provides perspective for potential opportunities and strategies for the successful integration and utilization of ML in PMX.

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“…All methods for model building, selection, identifiability, evaluation, and model qualification should be described. The methods utilized will vary based on the available data, the objectives of the analysis.…”

Section: “How” Should Mid3 Be Performed? Planning Conduct and Domentioning

confidence: 99%

Good Practices in Model‐Informed Drug Discovery and Development: Practice, Application, and Documentation

Burghaus

Cosson

et al. 2016

CPT Pharmacom & Syst Pharma

281

137

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This document was developed to enable greater consistency in the practice, application, and documentation of Model‐Informed Drug Discovery and Development (MID3) across the pharmaceutical industry. A collection of “good practice” recommendations are assembled here in order to minimize the heterogeneity in both the quality and content of MID3 implementation and documentation. The three major objectives of this white paper are to: i) inform company decision makers how the strategic integration of MID3 can benefit R&D efficiency; ii) provide MID3 analysts with sufficient material to enhance the planning, rigor, and consistency of the application of MID3; and iii) provide regulatory authorities with substrate to develop MID3 related and/or MID3 enabled guidelines.

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Structural identifiability and indistinguishability of compartmental models

Cited by 23 publications

References 34 publications

Inference of complex biological networks: distinguishability issues and optimization-based solutions

Inference of complex biological networks: distinguishability issues and optimization-based solutions

Machine learning in pharmacometrics: Opportunities and challenges

Good Practices in Model‐Informed Drug Discovery and Development: Practice, Application, and Documentation

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