Impact of the use of a drug-drug interaction checker on pharmacist interventions involving well-known strong interactors.

OBJECTIVES: Several 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. METHODS: The 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. RESULTS: Of 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. CONCLUSIONS: DDI 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.

A PK-PD model linking biomarker dynamics to progression-free survival in patients treated with everolimus and sorafenib combination therapy, EVESOR phase I trial.

PURPOSE: The objective was to develop a pharmacokinetic-pharmacodynamic (PK-PD) model linking everolimus and sorafenib exposure with biomarker dynamics and progression-free survival (PFS) based on data from EVESOR trial in patients with solid tumors treated with everolimus and sorafenib combination therapy and to simulate alternative dosing schedules for sorafenib. PATIENTS AND METHODS: Everolimus (5-10 mg once daily, qd) and sorafenib (200-400 mg twice daily, bid) were administered according to four different dosing schedules in 43 solid tumor patients. Rich PK and PD sampling for serum angiogenesis biomarkers was performed. Baseline activation of RAS/RAF/ERK (MAPK) pathway was assessed by quantification of mRNA specific gene panel in tumor biopsies. The PK-PD modeling was performed using NONMEM® software. RESULTS: An indirect response PK-PD model linking sorafenib plasma exposure with soluble vascular endothelial growth factor receptor 2 (sVEGFR2) dynamics was developed. Progression-free survival (PFS) was described by a parametric time-to-event model. Higher decreases in sVEGFR2 at day 21 and higher baseline activation of MAPK pathway were associated with longer PFS (p = 0.002 and p = 0.007, respectively). The simulated schedule sorafenib 200 mg bid 5 days-on/2 days-off + continuous everolimus 5 mg qd was associated with median PFS of 4.3 months (95% CI 1.6-14.4), whereas the median PFS in the EVESOR trial was 3.6 months (95% CI 2.7-4.2, n = 43). CONCLUSION: Sorafenib 200 mg bid 5 days-on/2 days-off + everolimus 5 mg qd continuous was selected for an additional arm of EVESOR trial to evaluate whether this simulated schedule is associated with higher clinical benefit. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01932177.

Clinical results of the EVESOR trial, a multiparameter phase I trial of everolimus and sorafenib combination in solid tumors.

PURPOSE: Everolimus (EVE) and sorafenib (SOR) combination was associated with synergistic activity in preclinical models. However, previous clinical studies were hampered by cumulative toxicities when both were given continuously. The academic EVESOR trial (NCT01932177) was designed to assess alternative doses and intermittent dosing schedules of EVE and SOR combination therapy to improve the benefit-risk ratio for patients with solid tumors. METHODS: EVESOR is a multiparameter dose-escalation phase I trial investigating different doses and dosing schedules, with the final objective of generating data for modeling and simulation. Patients were allocated into continuous (A and B) or intermittent (C and D) schedules to determine the recommended phase II dose (RP2D). The clinical outcomes are presented here. RESULTS: Forty-three patients were included from 2013 to 2019. Most of them had gynecological (25.6%), cholangiocarcinomas (23.2%), colorectal (14.0%), and breast cancers (11.6%). Dose-escalation up to EVE 10 mg QD and SOR 400 mg BID was possible on intermittent schedules. Five dose-limiting toxicities were observed, and dose reductions were required in 39.5% patients, stabilizing at EVE 5 mg and SOR 200 mg BID for 58.1% of them. The overall response rate was 6.3%, and disease control rate was 75.0%. The median progression-free survival (PFS) was 3.6 months. The longest median PFS were observed in cholangiocarcinomas (9.9 months), and gynecological adenocarcinomas (9.2 months). CONCLUSION: Intermittent arms were associated with improved efficacy/toxicity profiles; and EVE 5 mg QD and SOR 200 mg BID was defined a clinically feasible dose. Strong signs of efficacy were found in cholangiocarcinomas and gynecologic carcinomas. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01932177.

Considering the Oral Bioavailability of Protein Kinase Inhibitors: Essential in Assessing the Extent of Drug-Drug Interaction and Improving Clinical Practice.

Protein kinase inhibitors share pharmacokinetic (PK) pathways among themselves. They are all metabolized by several cytochromes P450 (CYP). For most of them, CYP3A4 is the predominant metabolic pathway. However, their oral bioavailability differs. For example, the oral bioavailability of imatinib has been estimated at nearly 100%, but that of ibrutinib averages 3% due to its high hepatic first-pass effect. Overall, the smaller the oral bioavailability, the larger its interindividual PK variability. Indeed, for drugs with low oral bioavailability, the extent of their absorption is an additional cause (along with elimination variability) of differences in drug exposure among patients. The impact of drug-drug interaction (DDI) also differs between drugs with low or high oral bioavailability. We describe and explain why the impact of CYP3A4 inhibitors and inducers is much greater for protein kinase inhibitors with low oral bioavailability. The effect of food on protein kinase inhibitors and DDIs corresponding to plasma protein binding will also be considered. Finally, the benefits of these concepts in clinical practice (including therapeutic drug monitoring) will be discussed. Overall, our main objective was to apply fundamental PK concepts to understanding the main clinical issues of these oral anticancer drugs.

Evaluation of Renal Impairment Influence on Metabolic Drug Clearance using a Modelling Approach.

BACKGROUND AND OBJECTIVE: Chronic kidney disease (CKD) may alter drug renal elimination but is also known for interacting with hepatic metabolism via multiple uremic components. However, few global models, considering the five major cytochromes, have been published, and none specifically address the decrease in cytochrome P450 (CYP450) activity. The aim of our study was to estimate the possibility of quantifying residual cytochrome activity as a function of filtration rate, according to the data available in the literature. METHODS: For each drug in the DDI-predictor database, we collected available pharmacokinetic data comparing drug exposition in the healthy patient and in various stages of CKD, before building a model capable of predicting the variation of exposure according to the degree of renal damage. We followed an In vivo Mechanistic Static Model (IMSM) approach, previously validated for predicting change in liver clearance. We estimated the remaining fraction parameters at glomerular filtration rate (GFR) = 0 and the alpha value of GFR to 50% impairment for the 5 major cytochromes using a non-linear constrained regression using Matlab software. RESULTS: Thirty-one compounds had usable pharmacokinetic data, with 51 AUC ratios between healthy and renal impaired patients. The remaining CYP3A4 activity was estimated to be 0.4 when CYP2D6, 2C9, 2C19 and 1A2 activity was estimated to be 0.43; 1; 0.73 and 0.7, respectively. The alpha value was estimated to be at 6.62; 25; 9.8; 1.38 and 11.04 for each cytochrome. In comparison with published data, all estimates but one were correctly predicted in the range of 0.5-2. CONCLUSION: Our approach was able to describe the impact of CKD on metabolic elimination. Modelling this process makes it possible to anticipate changes in clearance and drug exposure in CKD patients, with the advantage of greater simplicity than approaches based on physiologically-based pharmacokinetic modelling. However, a precise estimation of the impact of renal failure is not possible with an IMSM approach due to the large variability of the published data, and thus should rely on specific pharmacokinetic modelling for narrow therapeutic margin drugs.

Therapeutic Drug Monitoring and Pharmacogenetic Testing as Guides to Psychotropic Drug Dose Adjustment: An Observational Study.

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.

Paclitaxel with or without pazopanib for ovarian cancer relapsing during bevacizumab maintenance therapy: The GINECO randomized phase II TAPAZ study.

BACKGROUND: Anti-angiogenic rechallenge with bevacizumab plus chemotherapy is effective in recurrent ovarian cancer (rOC); however, data are limited on tyrosine kinase inhibitors after progression on maintenance bevacizumab. METHODS: In the randomized phase II TAPAZ trial, patients with rOC during the first year of bevacizumab maintenance therapy were assigned 2:1 to either weekly paclitaxel 65 mg/m2 plus pazopanib 600-800 mg daily or standard weekly paclitaxel 80 mg/m2. The primary endpoint was 4-month progression-free survival (PFS) rate. RESULTS: Overall, 116 patients were randomized and treated: 79 with combination therapy and 37 with single-agent paclitaxel. Median follow-up was 13.1 months. There was no difference between treatment arms in 4-month PFS rate (61% [95% CI, 51-73%] with the combination versus 68% [95% CI, 54-85%] with paclitaxel alone), median PFS (4.9 [95% CI, 4.1-6.1] versus 5.8 [95% CI, 4.8-7.4] months, respectively) or median overall survival (13.6 versus 12.9 months, respectively). The combination was associated with more grade 3/4 toxicities (87% versus 70%, respectively) and toxicity-related paclitaxel discontinuations (22% versus 11%). Pazopanib was discontinued for toxicity in 44% of patients, most commonly for gastrointestinal and vascular events. There were two treatment-related deaths, both in the combination arm (pulmonary embolism and gastrointestinal perforation). At month 4, patient-reported outcomes deteriorated from baseline in the combination arm, particularly for abdominal/gastrointestinal symptoms, which showed a clinically important difference versus paclitaxel alone. CONCLUSIONS: In rOC progressing during maintenance bevacizumab, adding pazopanib to paclitaxel did not improve efficacy, increased toxicity, and compromised chemotherapy delivery. CLINICALTRIALS: govregistration:NCT02383251.

Quantitative Prediction of Adverse Event Probability Due to Pharmacokinetic Interactions.

INTRODUCTION: Iatrogeny due to drug-drug interactions is insufficiently documented, due to the high number of possible combinations. OBJECTIVE: This study aimed to design a simple but general method to predict the variation of adverse events (AE) frequency due to a pharmacokinetic or pharmacodynamic interaction. METHODS: Three prediction models were designed using a logistic probability density function. Each prediction model was based on three components: the AE odds ratio of each drug in the combination, and the area under the curve ratio (Rauc) of the pharmacokinetic interaction, if any. Pharmacodynamic interaction was assumed to be additive on logit scale. Rauc was predicted using a well-validated mechanistic static model, freely available online. No combination study is required. The method was evaluated against a wide range of AEs (28 High Level Terms) and 211 drug combinations (involving 43 victim drugs and 55 perpetrators), by comparing the observed and predicted frequencies. The observed odds ratios were estimated with a disproportionality analysis from the FDA Adverse Event Reporting System, using an approach that minimizes biases. RESULTS: With the best model, the rate of prediction considered as correct (within 50-200% of the observed value) was 72%, and the bias was negligible (-5%). The AE odds ratio due to pharmacokinetic and pharmacodynamic interactions was equally well predicted. CONCLUSIONS: A simple workflow to implement the method in practice is proposed. This method may help to foresee and to anticipate the harmful consequences associated with drug-drug interactions, at virtually no experimental cost, when the odds ratio of an AE is known for each drug alone and the AUC ratio is known or predicted by a suitable model.

Low probability of disease cure in advanced ovarian carcinomas before the PARP inhibitor era.

BACKGROUND: In ovarian carcinomas, the likelihood of disease cure following first-line medical-surgical treatment has been poorly addressed. The objective was to: (a) assess the likelihood of long-term disease-free (LDF) > 5 years; and (b) evaluate the impact of the tumour primary chemosensitivity (assessed with the modelled CA-125 KELIM) with respect to disease stage, and completeness of debulking surgery. METHODS: Three Phase III trial datasets (AGO-OVAR 9; AGO-OVAR 7; ICON-7) were retrospectively investigated in an "adjuvant dataset", whilst the Netherlands Cancer Registry was used in a "neoadjuvant dataset". The prognostic values of KELIM, disease stage and surgery outcomes regarding the likelihood of LDF were assessed using univariate/multivariate analyses. RESULTS: Of 2029 patients in the "adjuvant dataset", 82 (4.0%) experienced LDF (Stage I-II: 25.9%; III: 2.1%; IV: 0.5%). Multivariate analyses identified disease stage and KELIM (OR = 4.24) as independent prognostic factors. Among the 1452 patients from the "neoadjuvant dataset", 36 (2.4%) had LDF (Stage II-III: 3.3%; IV: 1.3%). Using multivariate tests, high-risk diseases (OR = 0.18) and KELIM (OR = 2.96) were significant. CONCLUSION: The probability of LDF > 5 years after first-line treatment in 3486 patients (<4%) was lower than thought. These data could represent a reference for future studies meant to assess progress related to PARP inhibitors.

Modeled Early Longitudinal PSA Kinetics Prognostic Value in Rising PSA Prostate Cancer Patients after Local Therapy Treated with ADT +/- Docetaxel.

BACKGROUND: In metastatic prostate cancer (PCa) patients, androgen-deprivation therapy (ADT) combined with chemotherapy or next-generation androgen receptor targeted agents is a new standard treatment. The objective of the present study is to assess longitudinal PSA kinetics during treatment using mathematical modeling, to identify the modeled PSA kinetic parameters able to exhibit early prognostic/predictive values. METHODS: Phase III clinical trial dataset (NCT00764166) comparing ADT +/- docetaxel in 250 locally treated patients for PCa with rising PSA levels, who were at high risk of metastatic disease was assessed. A kinetic-pharmacodynamic (K-PD) model was used to fit PSA kinetics during the first 100 treatment days, to estimate the modeled PSA production rate K (KPROD) and elimination constant rate K (KELIM). The prognostic value of these parameters, considered as categorized (favorable vs. unfavorable) covariates regarding PSA progression-free survival (PSA-PFS) and overall survival (OS), was assessed using univariate/multivariate analyses. RESULTS: Data from 177/250 patients was assessed. KELIM exhibited a significant prognostic value regarding PSA-PFS and KPROD regarding OS (univariate analysis). In the PSA-PFS final multivariate model, KELIM and the primary therapy type were significant. The OS multivariate model integrated both KPROD and baseline PSA doubling-time. CONCLUSION: In this first study assessing the modeled PSA kinetics prognostic value in PCa patients treated with systemic treatments, KELIM and KPROD exhibited respective prognostic values regarding PSA-PFS and OS.

Integrated multiple analytes and semi-mechanistic population pharmacokinetic model of tusamitamab ravtansine, a DM4 anti-CEACAM5 antibody-drug conjugate.

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.

Covariate analysis of tusamitamab ravtansine, a DM4 anti-CEACAM5 antibody-drug conjugate, based on first-in-human study.

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.

Association between voriconazole exposure and Sequential Organ Failure Assessment (SOFA) score in critically ill patients.

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.

Population Pharmacokinetics and Dosing Simulation of Vancomycin Administered by Continuous Injection in Critically Ill Patient.

Background: Vancomycin is widely used for empirical antimicrobial therapy in critically ill patients with sepsis. Continuous infusion (CI) may provide more stable exposure than intermittent infusion, but optimal dosing remains challenging. The aims of this study were to perform population pharmacokinetic (PK) analysis of vancomycin administered by CI in intensive care unit (ICU) patients to identify optimal dosages. Methods: Patients who received vancomycin by CI with at least one measured concentration in our center over 16 months were included, including those under continuous renal replacement therapy (CRRT). Population PK was conducted and external validation of the final model was performed in a dataset from another center. Simulations were conducted with the final model to identify the optimal loading and maintenance doses for various stages of estimated creatinine clearance (CRCL) and in patients on CRRT. Target exposure was defined as daily AUC of 400-600 mg·h/L on the second day of therapy (AUC24-48 h). Results: A two-compartment model best described the data. Central volume of distribution was allometrically scaled to ideal body weight (IBW), whereas vancomycin clearance was influenced by CRRT and CRCL. Simulations performed with the final model suggested a loading dose of 27.5 mg/kg of IBW. The maintenance dose ranged from 17.5 to 30 mg/kg of IBW, depending on renal function. Overall, simulation showed that 55.8% (95% CI; 47-64%) of patients would achieve the target AUC with suggested dosages. Discussion: A PK model has been validated for vancomycin administered by CI in ICU patients, including patients under CRRT. Our model-informed precision dosing approach may help for early optimization of vancomycin exposure in such patients.

Cost of off-label antibiotic therapy for bone and joint infections: a 6-year prospective monocentric observational cohort study in a referral centre for management of complex osteo-articular infections.

Introduction: Costs related to bone and joint infection (BJI) management are increasing worldwide, particularly due to the growing use of off-label antibiotics that are expensive treatments (ETs), in conjunction with increasing incidence of multi-drug-resistant pathogens. The aim of this study was to evaluate the whole costs related to these treatments during the patient route, including those attributed to the rehabilitation centre (RC) stay in one regional referral centre in France. The total annual cost of ETs for managing complex BJIs in France was then estimated. Material and methods: A prospective monocentric observational study was conducted from 2014 to 2019 in a referral centre for BJI management (CRIOAc - Centre de Référence des Infections OstéoArticulaires complexes). Costs related to expensive treatments ("old" ETs, i.e. ceftaroline, ertapenem, daptomycin, colistin, tigecycline, and linezolid and "new" ETs, defined as those used since 2017, including ceftobiprole, ceftazidime-avibactam, ceftolozane-tazobactam, tedizolid, and dalbavancin) were prospectively recorded. In all cases, the use of these ETs was validated during multidisciplinary meetings. Results: Of the 3219 patients treated, 1682 (52.3 %) received at least one ET, and 21.5 % of patients who received ET were managed in RCs. The overall cost of ETs remained high but stable (EUR 1 033 610 in 2014; EUR 1 129 862 in 2019), despite the increase of patients treated by ETs (from 182 in 2014 to 512 in 2019) and in the cumulative days of treatment (9739 to 16 191 d). Daptomycin was the most prescribed molecule (46.2 % of patients in 2014 and 56.8 % in 2019, with 53.8 % overall), but its cost has decreased since this molecule was genericized in 2018; the same trend was observed for linezolid. Thus, costs for old ETs decreased overall, from EUR 1 033 610 in 2014 to EUR 604 997 in 2019, but global costs remained stable due to new ET utilization accounting for 46.5 % of overall costs in 2019. Tedizolid, used as suppressive antimicrobial therapy, represented 77.5 % of total new ET costs. In our centre, dalbavancin was never used. The cost paid by RCs for ETs and the duration of ET remained stable overall between 2016 and 2019. Conclusions: A high consumption of off-label ET is required to treat patients with BJIs in a CRIOAc, and the consequence is a high cost of antimicrobial therapy for these patients, estimated to be almost EUR 10 million in France annually. Costs associated with ET utilization remained stable over the years. On the one hand, the introduction of the generic drugs of daptomycin and linezolid has significantly decreased the share of old ETs, but, on the other hand, the need for new ETs to treat infections associated with more resistant pathogens has not led to decrease in the overall costs. A drastic price reduction of generic drugs is essential to limit the costs associated with more complex BJIs.

Model-Based Comparative Analysis of Rifampicin and Rifabutin Drug-Drug Interaction Profile.

Rifamycins are widely used for treating mycobacterial and staphylococcal infections. Drug-drug interactions (DDI) caused by rifampicin (RIF) are a major issue. We used a model-based approach to predict the magnitude of DDI with RIF and rifabutin (RBT) for 217 cytochrome P450 (CYP) substrates. On average, DDI caused by low-dose RIF were twice as potent as those caused by RBT. Contrary to RIF, RBT appears unlikely to cause severe DDI, even with sensitive CYP substrates.

Does DDI-Predictor Help Pharmacists to Detect Drug-Drug Interactions and Resolve Medication Issues More Effectively?

The characterization of drug-drug interactions (DDIs) may require the use of several different tools, such as the thesaurus issued by our national health agency (i.e., ANSM), the metabolic pathways table from the Geneva University Hospital (GUH), and DDI-Predictor (DDI-P). We sought to (i) compare the three tools' respective abilities to detect DDIs in routine clinical practice and (ii) measure the pharmacist intervention rate (PIR) and physician acceptance rate (PAR) associated with the use of DDI-P. The three tools' respective DDI detection rates (in %) were measured. The PIRs and PARs were compared by using the area under the curve ratio given by DDI-P (RAUC) and applying a chi-squared test. The DDI detection rates differed significantly: 40.0%, 76.5%, and 85.2% for ANSM (The National Agency for the Safety of Medicines and Health Products), GUH and DDI-P, respectively (p < 0.0001). The PIR differed significantly according to the DDI-P's RAUC: 90.0%, 44.2% and 75.0% for RAUC ≤ 0.5; RAUC 0.5-2 and RAUC > 2, respectively (p < 0.001). The overall PAR was 85.1% and did not appear to depend on the RAUC category (p = 0.729). Our results showed that more pharmacist interventions were issued when details of the strength of the DDI were available. The three tools can be used in a complementary manner, with a view to refining medication adjustments.

Modeling Approach to Predict the Impact of Inflammation on the Pharmacokinetics of CYP2C19 and CYP3A4 Substrates.

PURPOSE: For 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). METHODS: The 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. RESULTS: Changes in pharmacokinetic profiles and parameters induced by inflammation seem to be captured accurately by the models. CONCLUSIONS: These findings suggest that the pharmacokinetics of CYP2C19 and CYP3A4 substrates can be predicted depending on the CRP concentration.

Pharmacogenetic-guided glimepiride therapy in type-2 diabetes mellitus: a cost-effectiveness study.

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.