Bruno Charpiat

Failure of traditional trough levels to predict tacrolimus concentrations.

The objective of this study was to estimate tacrolimus population parameter values and to evaluate the ability of the maximum a posteriori probability (MAP) Bayesian fitting procedure to predict tacrolimus blood levels, using the traditional strategy of monitoring only trough levels, for dosage individualization in liver transplant patients. Forty patients treated with tacrolimus after liver transplantation were studied during the early posttransplant phase (first 2 weeks). This phase was divided into four time periods (1–4 days, 5–7 days, 8–11 days, 12–14 days). Tacrolimus was administered twice daily. Approximately one determination of a tacrolimus trough level on whole blood was performed each day. The NPEM2 program was used to obtain population pharmacokinetic parameter values. With each individual pharmacokinetic parameter estimated by the MAP Bayesian method for a given period, the authors evaluated the prediction of future levels of tacrolimus for that patient for the next period. This evaluation of Bayesian fitting predictive performance was performed using the USC*PACK clinical software. Mean pharmacokinetic parameter values were in the same general range as previously published values obtained with richer data sets. However, during each period, the percentage of blood levels predicted within 20% did not exceed 40%. The traditional strategy of obtaining only trough whole blood levels does not provide enough dynamic information for the MAP Bayesian fitting procedure (the best method currently available) to be used for adaptive control of drug dosage regimens for oral tacrolimus. The authors suggest modifying the blood concentration monitoring scheme to add at least one other concentration measured during the absorptive or distributive phase to obtain more information about the behavior of the drug. D-Optimal design and similar strategies should be considered.

Quantitative Prediction of Cytochrome P450 (CYP) 2D6-Mediated Drug Interactions

AbstractBackground and Objectives: An approach was recently proposed for quantitative predictions of cytochrome P450 (CYP) 3A4-mediated drug-drug interactions. This approach relies solely on in vivo data. It is based on two characteristic parameters: the contribution ratio (CR; i.e. the fraction of victim drug clearance due to metabolism by a specific CYP) and the inhibition ratio (IR) of the inhibitor. Knowledge of these parameters allows forecasting of the ratio between the area under the plasma concentration-time curve (AUC) of the victim drug when the inhibitor is co-administered and the AUC of the victim drug administered alone. The goals of our study were to extend this method to CYP2D6-mediated interactions, to validate it, and to forecast the magnitude of a large number of interactions that have not been studied so far. Methods: A three-step approach was pursued. First, initial estimates of CRs and IRs were obtained by several methods, using data from the literature. Second, an external validation of these initial estimates was carried out, by comparing the predicted AUC ratios with the observed values. Third, refined estimates of CRs and IRs were obtained by orthogonal regression in a Bayesian framework. Results: Thirty-nine AUC ratios were available for external validation. The mean prediction error of the ratios was 0.31, while the mean prediction absolute error was 1.14. Seventy AUC ratios were available for the global analysis. Final estimates of CRs and IRs were obtained for 39 substrates and 11 inhibitors, respectively. The mean prediction error of the AUC ratios was 0.04, while the mean prediction absolute error was 0.51. Conclusions: Predictive distributions for 615 possible interactions were obtained, giving detailed information on some drugs or inhibitors that have been poorly studied so far, such as metoclopramide, bupropion and terbinafine.

A population pharmacokinetic model of cyclosporine in the early postoperative phase in patients with liver transplants, and its predictive performance with Bayesian fitting

The availability of personal computer programs to individualize drug regimens has stimulated interest in modeling population pharmacokinetics. This study used the NPEM2 software to determine cyclosporine population pharmacokinetic parameter values and distributions in a first group of 25 recipients of liver transplants during their first postoperative week. On a second group of 25 patients, the authors used these values to evaluate Bayesian predictive performance of cyclosporine blood concentrations with the USC*PACK PC program. During the study period, all the patients have been treated by continuous intravenous infusion. The one-compartment model pharmacokinetic parameter-the slope of volume to body weight (Vs) and the elimination rate constant (Kel) values found (mean values: Vs = 2.177 l/kg, Kel = 0.235 h(-1); median values: Vs = 1.559 l/kg, Kel = 0.163 h(-1); the percent coefficient of variation (Vs = 92%, Kel = 79%) appear reasonable and show the ability of NPEM2 to deal with sparse data. When the predictions were studied with day 1, day 2, or day 3 concentrations, predictive bias was respectively -0.030, -0.013, and 0.013 microg/ml, suggesting a greater clearance of cyclosporine immediately after surgery, the clearance decreasing in the days after. With the first three blood levels and the Bayesian fitting procedure, it was possible to predict at least half the subsequent measured blood levels of each patient accurately (within 20%) in more than three-quarters (76%) of the second group of recipients of transplants, and for 40% of patients the authors obtained accurate predictions in 100% of the subsequent blood levels. For a few patients (12%) they found quite poor predictions. The reason for this is unclear. The results suggest that this population model and the Bayesian fitting procedure using two or three blood levels can be reasonably and carefully used to control, in real time, cyclosporine blood levels in a majority of new patients with liver transplants.

Validation pharmaceutique des prescriptions hospitalières: Intervention et analyse sur cinq ans

Resume Introduction L’analyse et la validation pharmaceutique des prescriptions medicamenteuses hospitalieres, activites rendues obligatoires depuis 1991, demeurent peu repandues. La nature des interventions faites par un pharmacien et leur frequence sont mal connues. Objectif Decrire la nature et la frequence des interventions pharmaceutiques dans un hopital. Methodes Nous avons realise un bilan des interventions emises lors de l’analyse pharmaceutique des prescriptions au sein d’un service de chirurgie, d’un service de medecine et d’une unite d’hospitalisation de courte duree sur une periode de 5 ans. Resultats Sur 13 760 prescriptions analysees, 1 438 interventions ont ete realisees (10,5 %). Parmi celles-ci, 30,9 % ont concerne une interaction medicamenteuse, 20,2 % un changement de dose, notamment en cas d’insuffisance renale, 13,8 % une proposition de relais de la voie injectable par la voie orale et 4,1 % une incompatibilite physico-chimique lors de l’administration simultanee de 2 medicaments par la meme ligne de perfusion. Les 2 groupes de medicaments les plus concernes par les interactions medicamenteuses ont ete les fluoroquinolones administrees par voie orale et les anticoagulants oraux. L’amoxicilline, le buflomedil, l’ofloxacine et l’allopurinol ont ete les 4 molecules pour lesquelles des propositions d’adaptation de posologie ont ete formulees le plus frequemment. Melanges polyvitaminiques, omeprazole, antifongiquesimidazoles et fluoroquinolones ont ete les classes medicamenteuses ayant fait l’objet d’un grand nombre de propositions de relais de la voie injectable par la voie orale. Les incompatibilites physico-chimiques entre le furosemide et la nicardipine etaient les plus frequentes, ainsi que celles se rapportant aux anti-infectieux. Conclusion La pratique de la pharmacie clinique au sein des unites de soins contribue a l’optimisation therapeutique et a la prevention de la iatrogenie medicamenteuse.

How many patients and blood levels are necessary for population pharmacokinetic analysis? A study of a one compartment model applied to cyclosporine

Objective:This paper describes a method to determine the number of patients and the number of blood levels which are appropriate for a pharmacokinetic population analysis.Methods:We studied this question by performing 203 runs of population analysis, using the NPEM algorithm with a one compartment model, starting with only one patient and only one blood level, then 2 patients with one blood level each, until reaching 38 patients each with 5 blood levels. Data were obtained from liver transplant patients treated with cyclosporine.Results:For 2, 3, 4 or 5 blood levels, the values of median clearance (CL) converged and became almost equal after about 10 patients were studied. The value then remained stable and the variation was fairly small. With only one blood level per patient, the variation was greater. In contrast, with one blood level, median CL became similar to groups having 2, 3, and 4 blood levels only after about 35 patients had been studied, versus about 10. Similar results were found for the median values of the volume of distribution (V). For a one compartment model with parameters of V and CL, from 15 to 20 patients with 2 blood levels may be enough to perform a reasonable population pharmacokinetic analysis; the values of the pharmacokinetic parameters were very similar to those obtained with 3 to 5 blood levels and with more patients. However, a subpopulation probably requires more patients and at least 4 or 5 blood levels per patient to be recognised.Conclusions:Examination of converging pharmacokinetic parameter values by stepwise increases in the number of patients and blood levels appears to be a pragmatic and empirical approach to determine the possible number of patients and blood levels required for population pharmacokinetic analysis.

Prediction of future serum concentrations with Bayesian fitted pharmacokinetic models: results with data collected by nurses versus trained pharmacy residents.

Recording the times of dosage administration and serum sampling by trained personnel resulted in significantly greater adherence to the protocol of therapeutic drug monitoring and in significantly greater precision in the achievement of desired serum concentration goals of aminoglycoside therapy than when relatively untrained personnel recorded it as a comparatively un-emphasized part of their job. This was true even when only data of peak and trough serum concentrations were used. This study demonstrates that thoughtful data collection by appropriately trained nursing, pharmacy, or other clinical personnel is an essential part of therapeutic drug monitoring and plays a significant role in the optimal individualization of drug dosage regimens for patient care.

Extreme bradycardia due to multiple drug–drug interactions in a patient with HIV post-exposure prophylaxis containing lopinavir–ritonavir

We report a case of an extreme bradycardia in a cardiac patient, due to multiple drug–drug interactions. This case caused significant diagnostic problems and illustrates the interplay between pharmacokinetics and pharmacogenetics. A 51-year-old man with a history of hypertensive and ischaemic myocardiopathy (cardiac infarction 11 years ago), osteoarthritis and hypothyroidism, was treated daily with lacidipine 2 mg, ramipril 5 mg, levothyroxine 75 µg, rosuvastatin 20 mg, metoprolol 100 mg and acetylsalicylic acid 325 mg. The patient consulted after a possible exposure to HIV during unsafe sexual activity. The physician prescribed a post-exposure prophylaxis (PEP) containing tenofovir disoproxil fumarate 245 mg/emtricitabine 200 mg combination once daily plus lopinavir 400 mg/ritonavir 100 mg combination twice daily for 1 month. Forty-eight hours after starting PEP, the patient presented a vasovagal syncope with severe hypotension (blood pressure was 50/20 mmHg) and extreme bradycardia (20–25 beats min−1). An electrocardiogram showed complete atrioventricular block (AV). The patient recovered a regular sinus rhythm after treatment with isoprenaline. Results of all diagnostic tests, including cardiac enzymes, complete blood cell count, electrolytes and tomodensitometry were normal. Lopinavir/ritonavir, lacidipine, ramipril and metoprolol were discontinued. Raltegravir was prescribed on day 4. Lacidipine, ramipril were re-instated on day 7 and metoprolol on day 9. We suspected the involvement of multiple drug–drug interactions between ritonavir and metoprolol and/or lacidipine. Lopinavir, ritonavir, tenofovir and emtricitabine were quantified by high-performance liquid chromatography. The subject was genotyped for several allelic variants most commonly found in Caucasians. The genes CYP2D6, CYP3A4, CYP3A5 and ABCB1 were amplified to detect 26 single nucleotide polymorphisms on CYP2D6, the CYP3A5*3 alleles, the −392A>G variant on CYP3A4 and the variants 1236C>T on exon 12, 2677 G > T/A on exon 21, and 3435 C > T on exon 26 on ABCB1. CYP2D6 deletion and gene duplication were also assessed. Blood concentrations were analyzed approximately 12 h after the last dose of tenofovir/emtricitabine combination and 20 h after the last dose of lopinavir/ritonavir combination. Lopinavir, ritonavir, tenofovir and emtricitabine plasma concentrations were 8.40 mg l−1, 0.29 mg l−1, 0.059 mg l−1 and 0.12 mg l−1, respectively. The lopinavir plasma concentration was higher than usual (3 to 7 mg l−1); but the measurement was done only 3 days after treatment initiation while 2 weeks are required to see the maximal enzyme inducing effects of ritonavir. Tenofovir and emtricitabine plasma concentrations were in the normal range. The patient carried CYP2D6*4/*2 alleles. No duplication of the CYP2D6 gene was found. The patient was classified as an intermediate metabolizer of CYP2D6. He had no potential functional alteration of CYP3A as the CYP3A4*1B polymorphism was not found, and CYP3A5 was not expressed (*3/*3). The patient appeared to be a CYP3A normal metabolizer. He was homozygous for the haplotype 2677T, 1236T and 3435T on ABCB1. This haplotype TTT is associated with lower ABCB1 expression. The AV block and hypotension in this case is consistent with an overexposure to metoprolol and lacidipine. This overexposure may be due to multiple pharmacokinetic interactions with the protease inhibitors ritonavir and lopinavir and/or to genetic polymorphism. Metoprolol undergoes α-hydroxylation and O-demethylation by several CYPs (predominantly 2D6 but also 3A4) [1]. Ramipril is hydrolyzed by esterases to ramiprilat, which is glucuronoconjugated [2]. Lacidipine is oxidized to pyridine derivatives by CYP3A4 [3]. We have several reasons to conclude that the AV block and the hypotension in this case was primarily associated with co-administration of the lopinavir/ritonavir combination with metoprolol and lacidipine. First, the patient was asymptomatic while he started antiretroviral therapy. Second, discontinuation of lopinavir/ritonavir, lacidipine, ramipril and metoprolol, restored normal rhythm. Third, the re-introduction of lacidipine, ramipril and metoprolol without lopinavir/ritonavir induced no bradyarrythmia. So, the antiprotease combination might have induced a drug–drug interaction and consequently a disruption in the treatment. Ritonavir is a potent inhibitor of CYP3A4 and to a lesser extent, of CYP2D6. Ritonavir exerts a complex dose and time dependent inhibitory/induction effect on CYP3A and on the multidrug efflux P-glycoprotein (P-gp) [4]. It exerts a moderate dose-related inhibition of CYP2D6 [5]. Lopinavir is a substrate of CYP3A, P-gp and to a lesser extent CYP2D6 [6]. Metoprolol, a selective β1-adrenoceptor antagonist, undergoes significant first pass metabolism with approximately 85% of the dose converted mainly into an inactive metabolite via CYP2D6 in extensive metabolizers. Finally, lacidipine (a dihydropyridine calcium antagonist) is a CYP3A4 substrate. Because lacidipine is a P-gp inhibitor [7], it is also possibly a substrate of P-gp. Even though the ritonavir-metoprolol interaction has never been studied, a possible increase in the average plasma exposure to metoprolol is supported by theoretical considerations and several case reports [8–10]. One of these described a complete AV block with metoprolol after co-administration of paroxetine, another CYP2D6 inhibitor [5]. The in vitro Ki of ritonavir for CYP2D6 is 2.5 mg l−1[11], which is higher than the ritonavir concentration measured in plasma (0.3 mg l−1). Nevertheless, it has been shown that low-dose ritonavir (100 mg twice daily) increased desipramine (a substrate of CYP2D6) AUC by 1.26 [12]. Because the contribution of CYP2D6 to total clearance is similar for desipramine and metoprolol (fm = 0.85) [13], the same degree of interaction is expected. In addition, metoprolol is metabolized by CY3A4. Because the patient was an intermediate metabolizer for CYP2D6, the contribution of CYP3A4 to the total clearance of metoprolol was increased. The in vitro Ki of ritonavir for CYP3A4 is 0.05 mg l [11]. Hence inhibition of CYP3A4 by low dose ritonavir might have contributed to an increased metoprolol exposure. Co-administration of ritonavir with lacidipine, whose bioavailability is less than 50% [14], may increase exposure to lacidipine by inhibiting CYP3A4 and P-gp, resulting in hypotension. Although this interaction has only a theoretical basis, it is well described with other dihydropyridine calcium antagonists. The co-administration of nifedipine with ritonavir may significantly increase nifedipine exposure, resulting in toxicity [15]. Indinavir plus ritonavir increases the AUC of amlodipine [16]. A case of symptomatic orthostasis and heart block after starting antiretroviral therapy that included nelfinavir was reported in a man who was receiving extended release nifedipine [17]. From a pharmacogenetic point of view, the patient was an extensive metabolizer for CYP3A and low expressor of P-gp. The TTT haplotype found in this patient is associated with higher plasma concentrations of P-gp substrates leading to a potential increase of lacidipine bioavailability without an increase in its metabolism because of the CYP3A5 genotype [18]. This case raises several important points. Firstly, HIV PEP has a strong potential for drug–drug interactions in patients with comorbidities requiring long term medications. Secondly, as illustrated by our case report, these potential interactions have to be identified as they may lead to early near-fatal complications. Systematic pharmacogenetic analyses combined with pharmaceutical analysis of the prescriptions may help in understanding, forecasting and managing these interactions.

Tacrolimus trough levels before, during and after jejunostomy in a liver transplant patient: a case report.

Summary Although the feasibility of oral tacrolimus administration in the presence of jejunostomy has already been reported, few studies monitoring tacrolimus trough blood levels have been analyzed in detail, either during or after a jejunostomy closure. We report on our experience with a 34-year-old patient who underwent liver transplantations, with a proximal jejunostomy constructed a few days prior to the second transplantation. He was administered tacrolimus by a predominantly oral route, and less frequently received it by jejunostomy. The aim of this paper is to discuss this administration strategy and whether a different method could have been more suitable. This case report highlights that during the jejunostomy period, the tacrolimus doses that were required to maintain trough concentrations within the therapeutic range were four times higher than those administered after the closure of the jejunostomy. We observed an increase in the Dose-Normalized Trough Concentration (DNTC) values when tacrolimus was administered for 4 consecutive days by jejunostomy as compared to oral administration, indicating that the relative bioavailability of tacrolimus increased. Moreover, when returning to oral administration, the subsequent DNTC value was halved, highlighting a reduction in the tacrolimus bioavailability. Thus, in such a case, administration by jejunostomy could be more appropriate.