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Precision medicine requires accurate technologies for drug administration and proper systems pharmacology approaches for patient data analysis. Here, plasma pharmacokinetics (PK) data of the OPTILIV trial in which cancer patients received oxaliplatin, 5-fluorouracil and irinotecan via chronomodulated schedules delivered by an infusion pump into the hepatic artery were mathematically investigated. A pump-to-patient model was designed in order to accurately represent the drug solution dynamics from the pump to the patient blood. It was connected to semi-mechanistic PK models to analyse inter-patient variability in PK parameters. Large time delays of up to 1h41 between the actual pump start and the time of drug detection in patient blood was predicted by the model and confirmed by PK data. Sudden delivery spike in the patient artery due to glucose rinse after drug administration accounted for up to 10.7% of the total drug dose. New model-guided delivery profiles were designed to precisely lead to the drug exposure intended by clinicians. Next, the complete mathematical framework achieved a very good fit to individual time-concentration PK profiles and concluded that inter-subject differences in PK parameters was the lowest for irinotecan, intermediate for oxaliplatin and the largest for 5-fluorouracil. Clustering patients according to their PK parameter values revealed patient subgroups for each drug in which inter-patient variability was largely decreased compared to that in the total population. This study provides a complete mathematical framework to optimize drug infusion pumps and inform on inter-patient PK variability, a step towards precise and personalized cancer chronotherapy.

We propose a life-expectancy pay-off function (LEP) for determining optimal cancer treatment within a control theory framework. The LEP averages life expectancy over all future outcomes, outcomes that are determined by key events during therapy such as tumour elimination (cure) and patient death (including treatment related mortality). We analyse this optimisation problem for tumours treated with chemotherapy using tumour growth models based on ordinary differential equations. To incorporate tumour elimination we draw on branching processes to compute the probability distribution of tumour population extinction. To demonstrate the approach, we apply the LEP framework to simplified one-compartment models of tumour growth that include three possible outcomes: cure, relapse, or death during treatment. Using Pontryagin’s maximum principle (PMP) we show that the best treatment strategies fall into three categories: (i) continuous treatment at the maximum tolerated dose (MTD), (ii) no treatment, or (iii) treat-and-stop therapy, where the drug is given at the MTD and then halted before the treatment (time) horizon. Optimal treatment strategies are independent of the time horizon unless the time horizon is too short to accommodate the most effective (treat-and-stop) therapy. For sufficiently long horizons, the optimal solution is either no treatment (when treatment yields no benefit) or treat-and-stop. Patients, thus, split into an untreatable class and a treatable class, with patient demographics, tumour size, tumour response, and drug toxicity determining whether a patient benefits from treatment. The LEP is in principle parametrisable from data, requiring estimation of the rates of each event and the associated life expectancy under that event. This makes the approach suitable for personalising cancer therapy based on tumour characteristics and patient-specific risk profiles.

Despite intensive treatments including temozolomide (TMZ) administration, glioblastoma patient prognosis remains dismal and innovative therapeutic strategies are urgently needed. A systems pharmacology approach was undertaken to investigate TMZ pharmacokinetics‐pharmacodynamics (PK‐PD) incorporating the effect of local pH, tumor spatial configuration and micro‐environment. A hybrid mathematical framework was designed coupling ordinary differential equations describing the intracellular reactions, with a spatial cellular automaton to individualize the cells. A differential drug impact on tumor and healthy cells at constant extracellular pH was computationally demonstrated as TMZ‐induced DNA damage was larger in tumor cells as compared to normal cells due to less acidic intracellular pH in cancer cells. Optimality of TMZ efficacy defined as maximum difference between damage in tumor and healthy cells was reached for extracellular pH between 6.8 and 7.5. Next, TMZ PK‐PD in a solid tumor was demonstrated to highly depend on its spatial configuration as spread cancer cells or fragmented tumors presented higher TMZ‐induced damage as compared to compact tumor spheroid. Simulations highlighted that smaller tumors were less acidic than bigger ones allowing for faster TMZ activation and their closer distance to blood capillaries allowed for better drug penetration. For model parameters corresponding to U87 glioma cells, inter‐cell variability in TMZ uptake play no role regarding the mean drug‐induced damage in the whole cell population whereas this quantity was increased by inter‐cell variability in TMZ efflux which was thus a disadvantage in terms of drug resistance. Overall, this study revealed pH as a new potential target to significantly improve TMZ antitumor efficacy.

Between 30% and 70% of patients with breast cancer have pre-existing chronic conditions, and more than half are on long-term non-cancer medication at the time of diagnosis. Preliminary epidemiological evidence suggests that some non-cancer medications may affect breast cancer risk, recurrence, and survival. In this nationwide cohort study, we assessed the association between medication use at breast cancer diagnosis and survival. We included 235,368 French women with newly diagnosed non-metastatic breast cancer. In analyzes of 288 medications, we identified eight medications positively associated with either overall survival or disease-free survival: rabeprazole, alverine, atenolol, simvastatin, rosuvastatin, estriol (vaginal or transmucosal), nomegestrol, and hypromellose; and eight medications negatively associated with overall survival or disease-free survival: ferrous fumarate, prednisolone, carbimazole, pristinamycin, oxazepam, alprazolam, hydroxyzine, and mianserin. Full results are available online from an interactive platform ( https://adrenaline.curie.fr ). This resource provides hypotheses for drugs that may naturally influence breast cancer evolution. Preliminary epidemiological evidence suggests that some non-cancer medications may affect breast cancer risk, recurrence, and survival. In this study, the authors utilized a nationwide database of breast cancer patients to estimate the association between frequently used drugs taken prior to diagnosis and breast cancer prognosis. And they identified 16 drugs associated with breast cancer outcomes.

Cancer therapy continues to be plagued by modest therapeutic advances. This is particularly evident in glioblastoma multiforme (GBM) wherein treatment failures are attributed to intratumoral heterogeneity (ITH), a dynamic process of cell state transitions or plasticity. To address ITH, we introduce the concept of cell state‐directed (CSD) therapy through a quantitative systems pharmacology model of temozolomide (TMZ), a cornerstone of GBM drug therapy. The model consisting of multiple modules incorporated an epigenetic‐based gene transcription‐translation module that enabled CSD therapy. Numerous model simulations were conducted to demonstrate the potential impact of CSD therapy on TMZ activity. The simulations included those based on global sensitivity analyses to identify fragile nodes – MDM2 and XIAP – in the network, and also how an epigenetic modifier (birabresib) could overcome a mechanism of TMZ resistance. The positive results of CSD therapy on TMZ activity supports continued efforts to develop CSD therapy as a new anticancer approach.

The least toxic time (LTT) of irinotecan varied by up to 8 hours according to sex and genetic background in mice. The translational relevance was investigated within a randomized trial dataset, where no LTT stood out significantly in the whole population. 130 male and 63 female eligible patients with metastatic colorectal cancer were randomized to receive chronomodulated Irinotecan with peak delivery rate at 1 of 6 clock hours staggered by 4 hours on day 1, then fixed‐time chronomodulated Fluorouracil‐Leucovorin‐Oxaliplatin for 4 days, q3 weeks. The sex‐specific circadian characteristics of grade (G) 3‐4 toxicities were mapped with cosinor and time*sex interactions confirmed with Fisher's exact test. Baseline characteristics of male or female patients were similar in the six treatment groups. Main grade 3‐4 toxicities over six courses were diarrhea (males vs females, 39.2%; vs 46.0%), neutropenia (15.6% vs 15.0%), fatigue (11.5% vs 15.9%), and anorexia (10.0% vs 7.8%). They were reduced following irinotecan peak delivery in the morning for males, but in the afternoon for females, with statistically significant rhythms (P < .05 from cosinor) and sex*timing interactions (Fisher's exact test, diarrhea, P = .023; neutropenia, P = .015; fatigue, P = .062; anorexia, P = .032). Irinotecan timing was most critical for females, with grades 3‐4 ranging from 55.2% of the patients (morning) to 29.4% (afternoon) for diarrhea, and from 25.9% (morning) to 0% (afternoon) for neutropenia. The study results support irinotecan administration in the morning for males and in the afternoon for females, in order to minimize adverse events without impairing efficacy. In this randomized international trial, the hematologic and clinical toxicities of irinotecan were least following dosing in the morning for men and in the afternoon for women with colorectal cancer. Sex‐specific least toxic times of anticancer drugs need further identification in prospective clinical trials.

Circadian timing largely modifies efficacy and toxicity of many anticancer drugs. Recent findings suggest that optimal circadian delivery patterns depend on the patient genetic background. We present here a combined experimental and mathematical approach for the design of chronomodulated administration schedules tailored to the patient molecular profile. As a proof of concept we optimized exposure of Caco-2 colon cancer cells to irinotecan (CPT11), a cytotoxic drug approved for the treatment of colorectal cancer. CPT11 was bioactivated into SN38 and its efflux was mediated by ATP-Binding-Cassette (ABC) transporters in Caco-2 cells. After cell synchronization with a serum shock defining Circadian Time (CT) 0, circadian rhythms with a period of 26 h 50 (SD 63 min) were observed in the mRNA expression of clock genes REV-ERBα, PER2, BMAL1, the drug target topoisomerase 1 (TOP1), the activation enzyme carboxylesterase 2 (CES2), the deactivation enzyme UDP-glucuronosyltransferase 1, polypeptide A1 (UGT1A1), and efflux transporters ABCB1, ABCC1, ABCC2 and ABCG2. DNA-bound TOP1 protein amount in presence of CPT11, a marker of the drug PD, also displayed circadian variations. A mathematical model of CPT11 molecular pharmacokinetics-pharmacodynamics (PK-PD) was designed and fitted to experimental data. It predicted that CPT11 bioactivation was the main determinant of CPT11 PD circadian rhythm. We then adopted the therapeutics strategy of maximizing efficacy in non-synchronized cells, considered as cancer cells, under a constraint of maximum toxicity in synchronized cells, representing healthy ones. We considered exposure schemes in the form of an initial concentration of CPT11 given at a particular CT, over a duration ranging from 1 to 27 h. For any dose of CPT11, optimal exposure durations varied from 3h40 to 7h10. Optimal schemes started between CT2h10 and CT2h30, a time interval corresponding to 1h30 to 1h50 before the nadir of CPT11 bioactivation rhythm in healthy cells.

Quantitative systems pharmacology holds the promises of integrating results from laboratory animals or in vitro human systems into the design of human pharmacokinetic/pharmacodynamic (PK/PD) models allowing for precision and personalized medicine. However, reliable and general in vitro‐to‐in vivo extrapolation and interspecies scaling methods are still lacking. Here, we developed a translational strategy for the anticancer drug oxaliplatin. Using ex vivo PK data in the whole blood of the mouse, rat, and human, a model representing the amount of platinum (Pt) in the plasma and in the red blood cells was designed and could faithfully fit each dataset independently. A “purely physiologically‐based (PB)” scaling approach solely based on preclinical data failed to reproduce human observations, which were then included in the calibration. Investigating approaches in which one parameter was set as species‐specific, whereas the others were computed by PB scaling laws, we concluded that allowing the Pt binding rate to plasma proteins to be species‐specific permitted to closely fit all data, and guaranteed parameter identifiability. Such a strategy presenting the drawback of including all clinical datasets, we further identified a minimal subset of human data ensuring accurate model calibration. Next, a “whole body” model of oxaliplatin human PK was inferred from the ex vivo study. Its three remaining parameters were estimated, using one third of the available patient data. Remarkably, the model achieved a good fit to the training dataset and successfully reproduced the unseen observations. Such validation endorsed the legitimacy of our scaling methodology calling for its testing with other drugs.

Cancer cells typically divide with weaker synchronisation with the circadian clock than normal cells, with the degree of decoupling increasing with tumour maturity. Chronotherapy exploits this loss of synchronisation, using drugs with circadian-clock-dependent activity and timed infusion to balance the competing demands of reducing toxicity toward normal cells that display physiological circadian rhythms and of efficacy against the tumour. We analysed optimal chronotherapy for one-compartment nonlinear tumour growth models that were no longer synchronised with the circadian clock, minimising a cost function with a periodically driven running cost accounting for the circadian drug tolerability of normal cells. Using Pontryagin’s Minimum Principle (PMP), we show, for drugs that either increase the cell death rate or kill dividing cells, that optimal solutions are aperiodic bang–bang solutions with two switches per day, with the duration of the daily drug administration increasing as treatment progresses; for large tumours, optimal therapy can in fact switch mid treatment from aperiodic to continuous treatment. We illustrate this with tumours grown under logistic and Gompertz dynamics conditions; for logistic growth, we categorise the different types of solutions. Singular solutions can be applicable for some nonlinear tumour growth models if the per capita growth rate is convex. Direct comparison of the optimal aperiodic solution with the optimal periodic solution shows the former presents reduced toxicity whilst retaining similar efficacy against the tumour. We only found periodic solutions with a daily period in one-compartment exponential growth models, whilst models incorporating nonlinear growth had generic aperiodic solutions, and linear multi-compartments appeared to have long-period (weeks) periodic solutions. Our results suggest that chronotherapy-based optimal solutions under a harmonic running cost are not typically periodic infusion schedules with a 24 h period.

Protein polymerization consists in the aggregation of single monomers into polymers that may fragment. Fibrils assembly is a key process in amyloid diseases. Up to now, protein aggregation was commonly mathematically simulated by a polymer size-structured ordinary differential equations (ODE) system, which is infinite by definition and therefore leads to high computational costs. Moreover, this Ordinary Differential Equation-based modeling approach implies biological assumptions that may be difficult to justify in the general case. For example, whereas several ordinary differential equation models use the assumption that polymerization would occur at a constant rate independently of polymer size, it cannot be applied to certain protein aggregation mechanisms. Here, we propose a novel and efficient analytical method, capable of modelling and simulating amyloid aggregation processes. This alternative approach consists of an integro-Partial Differential Equation (PDE) model of coalescence-fragmentation type that was mathematically derived from the infinite differential system by asymptotic analysis. To illustrate the efficiency of our approach, we applied it to aggregation experiments on polyglutamine polymers that are involved in Huntington's disease. Our model demonstrates the existence of a monomeric structural intermediate [Formula: see text] acting as a nucleus and deriving from a non polymerizing monomer ([Formula: see text]). Furthermore, we compared our model to previously published works carried out in different contexts and proved its accuracy to describe other amyloid aggregation processes.