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PurposeFor decades, inflammation has been considered a cause of pharmacokinetic variability, mainly in relation to the inhibitory effect of pro-inflammatory cytokines on the expression level and activity of cytochrome P450 (CYP). In vitro and clinical studies have shown that two major CYPs, CYP2C19 and CYP3A4, are both impaired. The objective of the present study was to quantify the impact of the inflammatory response on the activity of both CYPs in order to predict the pharmacokinetic profile of their substrates according to systemic C-reactive protein (CRP).MethodsThe relationships between CRP concentration and both CYPs activities were estimated and validated using clinical data first on midazolam then on voriconazole. Finally, clinical data on omeprazole were used to validate the findings. For each substrate, a physiologically based pharmacokinetics model was built using a bottom-up approach, and the relationships between CRP level and CYP activities were estimated by a top-down approach. After incorporating the respective relationships, we compared the predictions and observed drug concentrations.ResultsChanges in pharmacokinetic profiles and parameters induced by inflammation seem to be captured accurately by the models.ConclusionsThese findings suggest that the pharmacokinetics of CYP2C19 and CYP3A4 substrates can be predicted depending on the CRP concentration.

Ovarian cancer is the gynecological cancer with the worst prognosis and the highest mortality rate because 75% of patients are diagnosed with advanced stage III–IV disease. About 50% of patients are now treated with neoadjuvant chemotherapy followed by interval debulking surgery (IDS). In that context, there is a need for accurate predictors of tumor primary chemosensitivity, as it may impact the feasibility of subsequent IDS. Across seven studies with more than 12,000 patients, including six large randomized clinical trials and a national cancer registry, along with a mega-analysis database with 5842 patients, the modeled CA-125 ELIMination rate constant K (KELIM), the calculation of which is based on the longitudinal kinetics during the first three cycles of platinum-based chemotherapy, was shown to be a reproducible indicator of tumor intrinsic chemosensitivity. Indeed, KELIM is strongly associated with the likelihood of complete IDS, subsequent platinum-free interval, progression-free survival, and overall survival, along with the efficacy of maintenance treatment with bevacizumab or veliparib. As a consequence, KELIM might be used to guide more subtly the medical and surgical treatments in a first-line setting. Moreover, it could be used to identify the patients with poorly chemosensitive disease, who will be the best candidates for innovative treatments meant to reverse the chemoresistance, such as cell cycle inhibitors or immunotherapy.

Background A simple method to predict drug–drug interactions mediated by cytochrome P450 enzymes (CYPs) on the basis of in vivo data has been previously applied for several CYP isoforms but not for CYP1A2. The objective of this study was to extend this method to drug interactions caused by CYP1A2 inhibitors and inducers. Methods First, initial estimates of the model parameters were obtained using data from the literature. Then, an external validation of these initial estimates was performed by comparing model-based predicted area under the concentration–time curve (AUC) ratios with observations not used in the initial estimation. Third, refined estimates of the model parameters were obtained by Bayesian orthogonal regression using Winbugs software, and predicted AUC ratios were compared with all available observations. Finally, predicted AUC ratios for all possible substrates–inhibitors and substrates–inducers were computed. Results A total of 100 AUC ratios were retrieved from the literature. Model parameters were estimated for 19 CYP1A2 substrate drugs, 26 inhibitors and seven inducers, including tobacco smoking. In the external validation, the mean prediction error of the AUC ratios was −0.22, while the mean absolute error was 0.97 (37 %). After the Bayesian estimation step, the mean prediction error was 0.11, while the mean absolute error was 0.43 (22 %). The AUC ratios for 625 possible interactions were computed. Conclusion This analysis provides insights into the interaction profiles of drugs poorly studied so far and can help to identify and manage significant interactions in clinical practice. Those results are now available to the community via a web tool ( http://www.ddi-predictor.org ).

Identifying predictive biomarkers of drug response is of key importance to improve therapy management and drug selection in cancer therapy. To date, the influence of drug exposure and pharmacogenetic variants on sorafenib-induced toxicity remains poorly documented. The aim of this pharmacokinetic/pharmacodynamic (PK/PD) study was to investigate the relationship between early toxicity and drug exposure or pharmacogenetic variants in unselected adult outpatients treated with single-agent sorafenib for advanced solid tumors. Toxicity was recorded in 54 patients on days 15 and 30 after treatment initiation and sorafenib exposure was assessed in 51 patients. The influence of polymorphisms in CYP3A5, UGT1A9, ABCB1 and ABCG2 was examined in relation to sorafenib exposure and toxicity. Clinical characteristics, drug exposure and pharmacogenetic variants were tested univariately for association with toxicities. Candidate variables with p<0.1 were analyzed in a multivariate analysis. Gender was the sole parameter independently associated with sorafenib exposure (p = 0.0008). Multivariate analysis showed that increased cumulated sorafenib (AUC(cum)) was independently associated with any grade ≥ 3 toxicity (p = 0.037); UGT1A9 polymorphism (rs17868320) with grade ≥ 2 diarrhea (p = 0.015) and female gender with grade ≥ 2 hand-foot skin reaction (p = 0.018). Using ROC curve, the threshold AUC(cum) value of 3,161 mg/L.h was associated with the highest risk to develop any grade ≥ 3 toxicity (p = 0.018). In this preliminary study, increased cumulated drug exposure and UGT1A9 polymorphism (rs17868320) identified patients at high risk for early sorafenib-induced severe toxicity. Further PK/PD studies on larger population are warranted to confirm these preliminary results.

ObjectivesSeveral drug–drug interaction (DDI) checkers such as DDI-Predictor have been developed to detect and grade DDIs. DDI-Predictor gives an estimate of the magnitude of an interaction based on the ratio of areas under the curve. The objective of the present study was to analyse the frequencies of DDIs involving well-known strong interactors such as rifampicin and selective serotonin reuptake inhibitors (SSRIs), as reported by a clinical pharmacy team using DDI-Predictor, and the pharmacist intervention acceptance rate.MethodsThe pharmacist intervention rate and the physician acceptance rate were calculated for DDIs involving rifampicin or the SSRIs fluoxetine, paroxetine, duloxetine and sertraline. The rates were compared with a bilateral χ2 test or Fisher’s exact test.ResultsOf the 284 DDIs recorded, 38 (13.4%) involved rifampicin and 78 (27.5%) involved SSRIs. The pharmacist intervention rate differed significantly (68.4% for rifampicin vs 48.8% for SSRIs; p=0.045) but the physician acceptance rate did not (84.6% for rifampicin vs 81.6% for SSRIs; p=1). Pharmaceutical interventions for SSRIs were more frequent when the ratio of the area under the drug concentration versus time curve in DDI-Predictor was >2. Pharmacists were more likely to issue a pharmacist intervention for DDIs involving rifampicin because of a high perceived risk of treatment failure and were less likely to issue a pharmacist intervention for DDIs involving an SSRI, except when the suspected interaction was strong.ConclusionsDDI checkers can help pharmacists to manage DDIs involving strong interactors. DDIs involving strong inhibitors versus a strong inducer differ with regard to their intervention and acceptance rates, notably due to the estimation of the magnitude of the DDI.

Therapeutic drug monitoring (TDM) is essential for voriconazole to ensure optimal drug exposure, mainly in critically ill patients for whom voriconazole demonstrated a large variability. The study aimed at describing factors associated with trough voriconazole concentrations in critically ill patients and evaluating the impact of voriconazole concentrations on adverse effects. A 2-year retrospective multicenter cohort study (NCT04502771) was conducted in six intensive care units. Adult patients who had at least one voriconazole TDM were included. Univariable and multivariable linear regression analyses were performed to identify predictors of voriconazole concentrations, and univariable logistic regression analysis, to study the relationship between voriconazole concentrations and adverse effects. During the 2-year study period, 70 patients were included. Optimal trough voriconazole concentrations were reported in 37 patients (52.8%), subtherapeutic in 20 (28.6%), and supratherapeutic in 13 (18.6%). Adverse effects were reported in six (8.6%) patients. SOFA score was identified as a factor associated with an increase in voriconazole concentration (p = 0.025), mainly in the group of patients who had SOFA score ≥ 10. Moreover, an increase in voriconazole concentration was shown to be a risk factor for occurrence of adverse effects (p = 0.011). In that respect, critically ill patients who received voriconazole treatment must benefit from a TDM, particularly if they have a SOFA score ≥ 10. Indeed, identifying patients who are overdosed will help to prevent voriconazole related adverse effects. This result is of utmost importance given the recognized COVID-19-associated pulmonary aspergillosis in ICU patients for whom voriconazole is among the recommended first-line treatment.

Tusamitamab ravtansine is an anti‐CEACAM5 antibody‐drug conjugate indicated in patients with solid tumors. Based on a previous developed semimechanistic model describing simultaneously pharmacokinetic (PK) of SAR408701, two of its active metabolites: DM4 and methyl‐DM4 and naked antibody, with integration of drug‐to‐antibody data, the main objective of the present analysis was to evaluate covariate’s impact in patients from phase I/II study (n = 254). Demographic and pathophysiologic baseline covariates were explored to explain interindividual variability on each entity PK parameter. Model parameters were estimated with good precision. Five covariates were included in the final PK model: body surface area (BSA), tumor burden, albumin, circulating target, and gender. Comparison of BSA‐adjusted dosing and flat dosing supported the current BSA‐based dosing regimen, to limit under and over exposure in patients with extreme BSA. Overall, this model characterized accurately the PKs of all entities and highlighted sources of PK variability. By integrating mechanistic considerations, this model aimed to improve understanding of the SAR408701 complex disposition while supporting key steps of clinical development.

To avoid the failures in therapy with psychotropic drugs, treatments can be personalized by applying the results of therapeutic drug monitoring and pharmacogenetic testing. The objective of the present single-center observational study was to describe the changes in psychotropic drug management prompted by therapeutic drug monitoring and pharmacogenetic testing, and to compare the effective drug concentration based on metabolic status with the dose predicted using an in silico decision tool for drug–drug interactions. The study was conducted in psychiatry wards at Lille University Hospital (Lille, France) between 2016 and 2020. Patients with data for at least one therapeutic drug monitoring session or pharmacogenetic test were included. Blood tests were performed for 490 inpatients (mainly indicated by treatment monitoring or failure) and mainly concerned clozapine (21.4%) and quetiapine (13.7%). Of the 617 initial therapeutic drug monitoring tests, 245 (40%) complied with good sampling practice. Of the patients, 51% had a drug concentration within the therapeutic range. Regardless of the drug concentration, the drug management did not change in 83% of cases. Thirty patients underwent pharmacogenetic testing (twenty-seven had also undergone therapeutic drug monitoring) for treatment failure; the plasma drug concentration was outside the reference range in 93% of cases. The patient’s metabolic status explained the treatment failure in 12 cases (40%), and prompted a switch to a drug metabolized by another CYP450 pathway in 5 cases (42%). Of the six tests that could be analyzed with the in silico decision tool, all of the drug concentrations after adjustment were included in the range estimated by the tool. Knowledge of a patient’s drug concentration and metabolic status (for CYD2D6 and CYP2C19) can help clinicians to optimize psychotropic drug adjustment. Drug management can be optimized with good sampling practice, support from a multidisciplinary team (a physician, a geneticist, and clinical pharmacist), and decision support tools.

Tusamitamab ravtansine (SAR408701) is an antibody-drug conjugate (ADC), combining a humanized monoclonal antibody (IgG1) targeting carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) and a potent cytotoxic maytansinoid derivative, DM4, inhibiting microtubule assembly. SAR408701 is currently in clinical development for the treatment of advanced solid tumors expressing CEACAM5. It is administered intravenously as a conjugated antibody with an average Drug Antibody Ratio (DAR) of 3.8. During SAR408701 clinical development, four entities were measured in plasma: conjugated antibody (SAR408701), naked antibody (NAB), DM4 and its methylated metabolite (MeDM4), both being active. Average DAR and proportions of individual DAR species were also assessed in a subset of patients. An integrated and semi-mechanistic population pharmacokinetic model describing the time-course of all entities in plasma and DAR measurements has been developed. All DAR moieties were assumed to share the same drug disposition parameters, excepted for clearance which differed for DAR0 (i.e. NAB entity). The conversion of higher DAR to lower DAR resulted in a DAR-dependent ADC deconjugation and was represented as an irreversible first-order process. Each conjugated antibody was assumed to contribute to DM4 formation. All data were fitted simultaneously and the model developed was successful in describing the pharmacokinetic profile of each entity. Such a structural model could be translated to other ADCs and gives insight of mechanistic processes governing ADC disposition. This framework will further be expanded to evaluate covariates impact on SAR408701 pharmacokinetics and its derivatives, and thus can help identifying sources of pharmacokinetic variability and potential efficacy and safety pharmacokinetic drivers.

The demonstration of the link between certain genetic variations and drug response has allowed the emergence of pharmacogenetics, which offers many opportunities to improve patient care. Type-2 diabetes mellitus is a disease for which several gene polymorphisms have been reported to be associated with drug response. Sulfonylureas are commonly used for the management of this disease. Genetic polymorphisms of CYP2C9, the main enzyme involved in the metabolism of sulfonylureas, have been associated with the risk of severe hypoglycaemia, particularly in poor metabolizers carrying CYP2C9 *3/*3 genotype, and especially in the case of patients treated with glimepiride. The objectives of the present study were to evaluate the potential clinical and economic outcomes of using CYP2C9 genotype data to guide the management of SU regimen in patients initiating glimepiride therapy, and to identify factors affecting the cost-effectiveness of this treatment scheme. The analysis was conducted using a decision tree, considering a 1-year time horizon, and taking as perspective that of the French national health insurance system. With pharmacogenetic-guided therapy, the cost to avoid an episode of severe hypoglycaemia event per 100 000 patients treated was €421 834. Genotyping cost was the most influential factor on the incremental cost-effectiveness ratio. In conclusion, the potential cost of CYP2C9 genotype-guided dosing for glimepiride therapy is relatively high, and associated with modest improvements with respect to the number of hypoglycaemia avoided, as compared with standard dosing. Additional economic studies are required to better specify the usefulness of CYP2C9 genotyping prior to glimepiride regimen initiation.