Mechanistic model based meta-analysis for paediatric respiratory tract infection prophylaxis trial design

Arsène, Simon; Chevalier, Aude; Couty, Claire; Faddeenkov, Igor; Go, Natacha; Granjeon-Noriot, Solène; Kulesza, Alexander; Lehr, Lorenz; Pasquali, Christian

doi:10.1183/13993003.congress-2021.pa3152

Cited by 3 publications

(1 citation statement)

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“…Quantitative agreement of modeling predictions with experimental data cannot be accurate on individual level due to significant heterogeneity of biological objects and unavoidable uncertainly in individual quantitative characteristics. This issue can be addressed with in silico trials simulating treatment outcomes for heterogeneous virtual populations [15]. Robust conclusions gained by mathematical modeling can be verified in clinical trials and eventually be implemented into clinical decision-making pipeline [16].…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers

Kuznetsov,

Adhikarla,

Caserta

et al. 2024

Preprint

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Targeted radionuclide therapy is based on injections of cancer-specific molecules conjugated with radioactive nuclides. Despite the specificity of this treatment, it is not devoid of side-effects limiting its use and is especially harmful for rapidly proliferating organs well perfused by blood, like bone marrow. Optimization of radioconjugates administration accounting for toxicity constraints can increase treatment efficacy. Based on our experiments on disseminated multiple myeloma mouse model treated by225Ac-DOTA-daratumumab, we developed a mathematical model which investigation highlighted the following principles for optimization of targeted radionuclide therapy. 1) Nuclide to antibody ratio importance. The density of radioconjugates on cancer cells determines the density of radiation energy deposited in them. Low labeling ratio as well as accumulation of unlabeled antibodies and antibodies attached to decay products in the bloodstream can mitigate cancer radiation damage due to excessive occupation of specific receptors by antibodies devoid of radioactive nuclides. 2) Cancer binding capacity-based dosing. The rate of binding of drug to cancer cells depends on the total number of their specific receptors, which therefore can be estimated from the pharmacokinetic curve of diagnostic radioconjugates. Injection of doses significantly exceeding cancer binding capacity should be avoided since radioconjugates remaining in the bloodstream have negligible efficacy to toxicity ratio. 3) Particle range-guided multi-dosing. The use of short-range particle emitters and high-affinity antibodies allows for robust treatment optimization via initial saturation of cancer binding capacity, enabling redistribution of further injected radioconjugates and deposited dose towards still viable cells that continue expressing specific receptors.SignificanceMathematical modeling yields general principles for optimization of targeted radionuclide therapy in mouse models of multiple myeloma that can be extrapolated on another cancer models and on clinical setting.

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Section: Introductionmentioning

confidence: 99%

Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers

Kuznetsov,

Adhikarla,

Caserta

et al. 2024

Preprint

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In Silico Clinical Trials: Is It Possible?

Arsène,

Parès,

Tixier

et al. 2023

Methods in Molecular Biology

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Modeling the disruption of respiratory disease clinical trials by non-pharmaceutical COVID-19 interventions

Arsène¹,

Couty²,

Faddeenkov³

et al. 2022

Nat Commun

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Respiratory disease trials are profoundly affected by non-pharmaceutical interventions (NPIs) against COVID-19 because they perturb existing regular patterns of all seasonal viral epidemics. To address trial design with such uncertainty, we developed an epidemiological model of respiratory tract infection (RTI) coupled to a mechanistic description of viral RTI episodes. We explored the impact of reduced viral transmission (mimicking NPIs) using a virtual population and in silico trials for the bacterial lysate OM-85 as prophylaxis for RTI. Ratio-based efficacy metrics are only impacted under strict lockdown whereas absolute benefit already is with intermediate NPIs (eg. mask-wearing). Consequently, despite NPI, trials may meet their relative efficacy endpoints (provided recruitment hurdles can be overcome) but are difficult to assess with respect to clinical relevance. These results advocate to report a variety of metrics for benefit assessment, to use adaptive trial design and adapted statistical analyses. They also question eligibility criteria misaligned with the actual disease burden.

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Mechanistic model based meta-analysis for paediatric respiratory tract infection prophylaxis trial design

Cited by 3 publications

References 0 publications

Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers

Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers

In Silico Clinical Trials: Is It Possible?

Modeling the disruption of respiratory disease clinical trials by non-pharmaceutical COVID-19 interventions

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