In Silico Clinical Trials: Is It Possible?

Arsène, Simon; Parès, Yves; Tixier, Eliott; Granjeon-Noriot, Solène; Martin, Bastien; Bruezière, Lara; Couty, Claire; Courcelles, Eulalie; Kahoul, Riad; Pitrat, Julie; Go, Natacha; Monteiro, Claudio; Kleine-Schultjann, Julie; Jemai, Sarah; Pham, Emmanuel; Boissel, Jean-Pierre; Kulesza, Alexander

doi:10.1007/978-1-0716-3449-3_4

Cited by 6 publications

(3 citation statements)

References 130 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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: Mathematical Backgroundmentioning

confidence: 99%

See 1 more Smart Citation

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

Kuznetsov,

Adhikarla,

Caserta

et al. 2024

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Mathematical Backgroundmentioning

confidence: 99%

“…S. 15 shows, remains less toxic and delivers greater fraction of injected dose to viable cells. This is explained by the fact that, independently of the value of k s , during single-dose treatment the efficacy of radiation damage cannot decrease with time slower than e −λt , with the slowest decrease accompanying cross-fire-only case.…”

mentioning

confidence: 99%

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

Kuznetsov,

Adhikarla,

Caserta

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…-In silico human clinical trial simulations for improving risk assessment and decision-making (Arsène et al, 2024). These capabilities will be increasingly vital for implementing green toxicology frameworks that minimize animal testing while still allowing rapid evaluation of chemical hazards and risks.…”

Section: Avoiding Regrettable Substitutionsmentioning

confidence: 99%

Alternative methods go green! Green toxicology as a sustainable approach for assessing chemical safety and designing safer chemicals

2024

ALTEX

View full text Add to dashboard Cite

Green toxicology is marching chemistry into the 21 st century. This emerging framework will transform how chemical safety is evaluated by incorporating evaluation of the hazards, exposures, and risks associated with chemicals into early product development in a way that minimizes adverse impacts on human and environmental health. The goal is to minimize toxic threats across entire supply chains through smarter designs and policies. Traditional animal testing methods are replaced by faster, cutting-edge innovations like organs-on-chips and artificial intelligence predictive models that are also more cost-effective. Core principles of green toxicology include utilizing alternative test methods, applying the precautionary principle, considering lifetime impacts, and emphasizing risk prevention over reaction. This paper provides an overview of these foundational concepts and describes current initiatives and future opportunities to advance the adoption of green toxicology approaches. Challenges and limitations are also discussed. Green shoots are emerging with governments offering carrots like the European Green Deal to nudge industry. Noteworthy, animal rights and environmental groups have different ideas about the needs for testing and their consequences for animal use. Green toxicology represents the way forward to support both these societal needs with sufficient throughput and human relevance for hazard information and minimal animal suffering. Green toxicology thus sets the stage to synergize human health and ecological values. Overall, the integration of green chemistry and toxicology has potential to profoundly shift how chemical risks are evaluated and managed to achieve safety goals in a more ethical, ecologically-conscious manner.

show abstract

Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice

Kulesza,

Couty,

Lemarre

et al. 2024

J Pharmacokinet Pharmacodyn

View full text Add to dashboard Cite

In Silico Clinical Trials: Is It Possible?

Cited by 6 publications

References 130 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

Alternative methods go green! Green toxicology as a sustainable approach for assessing chemical safety and designing safer chemicals

Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice

Contact Info

Product

Resources

About