Applications of mechanistic modelling to clinical and experimental immunology: an emerging technology to accelerate immunotherapeutic discovery and development

Brown, Liam; Gaffney, Eamonn A.; Wagg, Jonathan; Coles, Mark

doi:10.1111/cei.13182

Cited by 9 publications

(6 citation statements)

References 42 publications

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“…Mathematical in silico models have been previously used to predict and model pharmacological and biological processes [21][22][23][24] . We first administered radiolabeled CLR01 systemically to 12month-old animals to measure the blood-brain penetration of the compound to inform an in silico model that would allow us to determine whether a monthly dose of 40 µg/kg/day, which was previously used 8 , would provide favorable pharmacokinetic profile in the brain.…”

Section: Resultsmentioning

confidence: 99%

CLR01 protects dopaminergic neurons in vitro and in mouse models of Parkinson’s disease

et al. 2020

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Parkinson’s disease (PD) affects millions of patients worldwide and is characterized by alpha-synuclein aggregation in dopamine neurons. Molecular tweezers have shown high potential as anti-aggregation agents targeting positively charged residues of proteins undergoing amyloidogenic processes. Here we report that the molecular tweezer CLR01 decreased aggregation and toxicity in induced pluripotent stem cell-derived dopaminergic cultures treated with PD brain protein extracts. In microfluidic devices CLR01 reduced alpha-synuclein aggregation in cell somas when axonal terminals were exposed to alpha-synuclein oligomers. We then tested CLR01 in vivo in a humanized alpha-synuclein overexpressing mouse model; mice treated at 12 months of age when motor defects are mild exhibited an improvement in motor defects and a decreased oligomeric alpha-synuclein burden. Finally, CLR01 reduced alpha-synuclein-associated pathology in mice injected with alpha-synuclein aggregates into the striatum or substantia nigra. Taken together, these results highlight CLR01 as a disease-modifying therapy for PD and support further clinical investigation.

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Section: Resultsmentioning

confidence: 99%

CLR01 protects dopaminergic neurons in vitro and in mouse models of Parkinson’s disease

et al. 2020

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“…refs. [ 4–6 ]), but in silico experimentation and combined experimental-modelling studies are less common [ 30 ]. This approach holds great potential to accelerate discovery in human medicine in the coming years.…”

Section: Discussionmentioning

confidence: 99%

De-risking clinical trial failure through mechanistic simulation

Brown

Wagg

Darley

et al. 2022

Immunotherapy Advances

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Drug development typically comprises a combination of pre-clinical experimentation, clinical trials and sta- tistical data driven analyses. Therapeutic failure in late-stage clinical development costs the pharmaceutical industry billions of USD per year. Clinical trial simulation represents a key derisking strategy and combining them with mechanistic models allows one to test hypotheses for mechanisms of failure and to improve trial designs. This is illustrated with a T-cell activation model, used to simulate the clinical trials of IMA901, a short-peptide cancer vaccine. Simulation results were consistent with observed outcomes and predicted that responses are limited by peptide off-rates, peptide competition for dendritic cell binding and dendritic cell mi- gration times. These insights were used to hypothesise alternate trial designs predicted to improve efficacy outcomes. This framework illustrates how mechanistic models can complement clinical, experimental and data driven studies to understand, test and improve trial designs, and how results may differ between humans and mice.

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“…evolution of glucose concentration in the plasma), while others are difficult to quantify in an experimental settings ( e.g. concentration of intracellular ATP in a specific cell) (Brown et al, 2018). To confidently draw conclusions from modeling analysis, the measurable variables of the model must reproduce what is observed in experimental settings, and the knowledge used to link measurable and non-measurable variables must be consistent with the scientific consensus.…”

Section: Introductionmentioning

confidence: 99%

Integration of heterogeneous biological data in multiscale mechanistic model calibration: application to lung adenocarcinoma

Palgen¹,

Perrillat-Mercerot²,

Ceres³

et al. 2022

Preprint

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Mechanistic models are built using knowledge as the primary information source, with well-established biological and physical laws determining the causal relationships within the model. Once the causal structure of the model is determined, parameters must be defined in order to accurately reproduce relevant data. Determining parameters and their values is particularly challenging in the case of models of pathophysiology, for which data for calibration is sparse. Multiple data sources might be required, and data may not be in a uniform or desirable format. We describe a calibration strategy to overcome the challenges of scarcity and heterogeneity of calibration data. Our strategy focuses on parameters where initial values cannot be easily derived from the literature and where final values are estimated via calibration with constraints set by relevant data. When combined with a covariance matrix adaptation evolution strategy (CMA-ES), this step-by-step approach can be applied to a wide range of biological models. We describe a stepwise, integrative and iterative approach to multiscale mechanistic model calibration, and provide an example of calibrating a pathophysiological lung adenocarcinoma model. Using the approach described here we illustrate the successful calibration of a complex knowledge-based mechanistic model using only the limited heterogeneous datasets publicly available in the literature.

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Applications of mechanistic modelling to clinical and experimental immunology: an emerging technology to accelerate immunotherapeutic discovery and development

Cited by 9 publications

References 42 publications

CLR01 protects dopaminergic neurons in vitro and in mouse models of Parkinson’s disease

CLR01 protects dopaminergic neurons in vitro and in mouse models of Parkinson’s disease

De-risking clinical trial failure through mechanistic simulation

Integration of heterogeneous biological data in multiscale mechanistic model calibration: application to lung adenocarcinoma

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