Population pharmacokinetic modeling of tramadol and its O-desmethyl metabolite in plasma and breast milk

The MILK study: Investigating intergenerational transmission of low-calorie sweeteners in breast milk

Introduction

Forty-four percent of lactating women in the United States consume beverages containing low calorie sweeteners (LCS), and the presence of LCS in the food supply has continued to increase in recent years. While LCS are approved by the United States Food and Drug Administration (FDA) and are believed to be safe for human consumption, intergenerational LCS transmission and the health impacts of early life LCS exposure are severely understudied.

Methods and analysis

In a tightly controlled, single site, prospective interventional study, mothers' plasma and breast milk, and infants' plasma will be collected from 40 mother-infant dyads over the course of 72 h, with rich sampling following maternal ingestion of a LCS sweetened beverage containing sucralose and acesulfame potassium (ace-K). Concentration-time data will be used to build maternal and infant pharmacokinetic models for future simulations and analysis.

Conclusion

This study aims to measure LCS concentrations in breast milk, maternal plasma, and infant plasma, to gain insight into infant exposure and inform recommendations for LCS consumption during breastfeeding.

Population pharmacokinetics and pharmacokinetic/pharmacodynamic evaluation of marbofloxacin against Coagulase-negative staphylococci, Staphylococcus aureus and Mycoplasma agalactiae pathogens in goats

Marbofloxacin is a broad-spectrum fluoroquinolone, and an extra-label use has been reported in horse, sheep and goat. However, extrapolation of dosage regimens from cattle to horse and small ruminants could lead to incorrect dosing due to pharmacokinetic differences among species, increasing the risk of antimicrobial resistance or toxicity. Pharmacokinetic properties of marbofloxacin, including PK/PD analysis, have been studied by intravenous, intramuscular and subcutaneous administration in lactating and non-lactating goats. A population pharmacokinetic model of marbofloxacin in goats was built using 10 pharmacokinetic studies after intravenous, intramuscular, and subcutaneous administration at a dose of 2, 5 and 10 mg/kg. Serum or plasma and milk concentration-time profiles were simultaneously fitted with a non-linear mixed effect model with Monolix software. Level of milk production (lactating and non-lactating) and health status (healthy and un-healthy) were retained as covariates on volume of distribution and clearance. Marbofloxacin concentrations were well described in plasma/serum and milk by the population model. Simulated dose regimens of marbofloxacin administered at 2, 5 and 10 mg/kg by intramuscular route for five days were evaluated (n = 5000 per group). Steady-state fAUCs for each dose regimen were obtained. Probability of target attainment of fAUC/MIC ratios were determined and PK/PDco values (highest MIC for which 90% of individuals can achieve a prior numerical value of the fAUC/MIC index) were established using Monte Carlo simulations (n = 50,000). MIC values for wild type isolates of Staphylococcus aureus, coagulase negative staphylococci, and Mycoplasma agalactiae were determined and tentative epidemiological cutoff (TECOFF) were obtained at 1.0, 0.5 and 0.5 mg/L, respectively. The PK/PDco for the dose regimen of 2 mg/kg/24 h and 5 mg/kg/24 h (0.125 and 0.25 mg/L) were lower than TECOFF (0.5 and 1 mg/L). The dosage regimen of 10 mg/kg/24 h was adequate for intermediate MIC values of 0.125-0.50 mg/L and could be effective for a population with a target fAUC/MIC ratio ˂ 48 for Coagulase negative staphylococci and Mycoplasma agalactiae, but not for Staphylococcus aureus. Results obtained in this study could be taken as a starting point by committees that set the clinical breakpoints and justifies expert rules to optimize marbofloxacin dose regimens.

Population Pharmacokinetic Model for Tramadol and O-desmethyltramadol in Older Patients

BACKGROUND AND OBJECTIVES

Tramadol is commonly prescribed to manage chronic pain in older patients. However, there is a gap in the literature describing the pharmacokinetic parameters for tramadol and its active metabolite (O-desmethyltramadol [ODT]) in this population. The objective of this study was to develop and evaluate a population pharmacokinetic model for tramadol and ODT in older patients.

METHODS

Twenty-one patients who received an extended-release oral tramadol dose (25-100 mg) were recruited. Tramadol and ODT concentrations were determined using a validated liquid chromatography/tandem mass spectrometry method. A population pharmacokinetic model was developed using non-linear mixed-effects modelling. The performance of the model was assessed by visual predictive check.

RESULTS

A two-compartment, first-order absorption model with linear elimination best described the tramadol concentration data. The absorption rate constant was 2.96/h (between-subject variability [BSV] 37.8%), apparent volume of distribution for the central compartment (V₁/F) was 0.373 l (73.8%), apparent volume of distribution for the peripheral compartment (V₂/F) was 0.379 l (97.4%), inter-compartmental clearance (Q) was 0.0426 l/h (2.19%) and apparent clearance (CL/F) was 0.00604 l/h (6.61%). The apparent rate of metabolism of tramadol to ODT (k_t) was 0.0492 l/h (78.5%) and apparent clearance for ODT (CL_m) was 0.143 l/h (21.6%). Identification of Seniors at Risk score (ISAR) and creatinine clearance (CrCL) were the only covariates included in the final model, where a higher value for the ISAR increased the maximum concentration (C_max) of tramadol and reduced the BSV in Q from 4.71 to 2.19%. A higher value of CrCL reduced tramadol C_max and half-life (T_1/2) and reduced the BSV in V₂/F (from 148 to 97.4%) and in CL/F (from 78.9 to 6.61%).

CONCLUSION

Exposure to tramadol increased with increased frailty and reduced CrCL. Prescribers should consider patients frailty status and CrCL to minimise the risk of tramadol toxicity in such cohort of patients.

Evaluation of the estimation and classification performance of NONMEM when applying mixture model for drug clearance

Maximum likelihood estimation of parameters involving mixture model is known to have significant and specific patterns of errors. Population pharmacokinetic (PopPK) modeling using NONMEM is no exception. A few relevant studies on estimation and classification performance were done, but a comprehensive study was not yet available. The current study aims to evaluate performance and likelihood ratio test (LRT)‐based true covariate detection rate when fitting a bimodal mixture of drug clearance (CL) in NONMEM. A large number of PopPK datasets with various settings were simulated and then estimated. The estimates were compared to the simulated values and summarized. The separation between the CL distributions of the two subpopulations is systematically overestimated. The major factor associated with the performance is the change in the minimum objective function value after removing the mixture component (dOFV). Other significant factors include estimated disparity index (DI), estimated mixing proportion, and number of subjects in the dataset. Small dOFV and large estimated DI are associated with the worst performance. Omitting a true mixture resulted in reduced true covariate detection rates. It is recommended that on top of routinely generated standard errors and model diagnostics, dOFV, and other factors when necessary, should be taken into account for the evaluation of performance when fitting mixture model using NONMEM. In addition, when fitting mixture model for CL is intended, the mixture component should be introduced prior to LRT‐based covariate model development for CL.

Novel models for the prediction of drug-gene interactions

INTRODUCTION

Adverse drug reactions (ADRs) are among the leading causes of death, and frequently associated with drug-gene interactions (DGIs). In addition to pharmacogenomic programs for implementation of genetic preemptive testing into clinical practice, mathematical modeling can help to understand, quantify and predict the effects of DGIs in vivo. Moreover, modeling can contribute to optimize prospective clinical drug trial activities and to reduce DGI-related ADRs.

AREAS COVERED

Approaches and challenges of mechanistical DGI implementation and model parameterization are discussed for population pharmacokinetic and physiologically based pharmacokinetic models. The broad spectrum of published DGI models and their applications is presented, focusing on the investigation of DGI effects on pharmacology and model-based dose adaptations.

EXPERT OPINION

Mathematical modeling provides an opportunity to investigate complex DGI scenarios and can facilitate the development process of safe and efficient personalized dosing regimens. However, reliable DGI model input data from in vivo and in vitro measurements are crucial. For this, collaboration among pharmacometricians, laboratory scientists and clinicians is important to provide homogeneous datasets and unambiguous model parameters. For a broad adaptation of validated DGI models in clinical practice, interdisciplinary cooperation should be promoted and qualification toolchains must be established.

Neonatal Exposure to Tramadol through Mother's Breast Milk

Tramadol is an opioid used in the treatment of moderate to moderately severe pain. Tramadol's use during pregnancy is generally avoided and may cause some reversible withdrawal effects in neonates and its use during lactation is not licensed by the manufacturer. A small clinical trial reported infants were exposed to < 3% of a mother's tramadol dose though breast milk with no evidence of harmful effects. Presented is a case study of breast milk, neonatal urine, and neonatal oral fluid for the analysis of tramadol and its metabolites, along with the validation of a method for the analysis of tramadol, O-desmethyltramadol, and N-desmethyltramadol in breast milk. Tramadol and its metabolites were extracted by solid-phase extraction (SPE) after saponification of breast milk to remove lipids. Samples were analyzed by ultra-pressure liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). To the author's knowledge, this is the first report of tramadol and its metabolites in neonatal oral fluid. The breast milk concentrations were 63, 22, and 76 ng/mL tramadol, O-desmethyltramadol and N-desmethyltramadol, respectively on day of life 12. On day of life 20, the breast milk concentrations were 1,254, 388, and 937 ng/mL tramadol, O-desmethyltramadol and N-desmethyltramadol, respectively. Oral fluid concentrations were 1,011, 1,499, and 406 ng/mL tramadol, O-desmethyltramadol and N-desmethyltramadol, respectively on day of life 20. Oral fluid concentrations were similar to breast milk for tramadol, almost four times higher for O-desmethyltramadol, and less than half for N-desmethyltramadol. The absolute infant dose was calculated to be 10 μg/kg/day and 294 μg/kg/day tramadol on day of life 12 and 20, respectively.

Infant exposure to dolutegravir through placental and breastmilk transfer: a population pharmacokinetic analysis of DolPHIN-1

BACKGROUND

Rapid reduction of HIV viral load is paramount to prevent peripartum transmission in women diagnosed late in pregnancy. We investigated dolutegravir population pharmacokinetics in maternal plasma, cord, breastmilk and infant plasma of DolPHIN-1 participants (NCT02245022) presenting with untreated HIV late in pregnancy (28-36 weeks gestation).

METHODS

Pregnant women from Uganda and South Africa were randomised (1:1) to daily dolutegravir (50 mg) or efavirenz-based therapy. Dolutegravir pharmacokinetic sampling (0-24 hours) was undertaken 14 days after treatment initiation and within 1-3 weeks of delivery, with matched maternal and cord samples at delivery. Mothers switched to efavirenz and maternal and infant plasma and breastmilk samples taken 24, 48 or 72 hours post-switch. Nonlinear mixed effects (NONMEM v. 7.4) was used to describe dolutegravir in all matrices and to evaluate covariates.

RESULTS

Twenty-eight women and 22 infants were included. Maternal dolutegravir was described by a two-compartment model linked to a fetal and breastmilk compartment. Cord and breastmilk to maternal plasma ratios were 1.279 (1.209-1.281) and 0.033 (0.021-0.050), respectively. Infant dolutegravir was described by breastmilk-to-infant and infant elimination rate constants. No covariate effects were observed. Predicted infant dolutegravir half-life and time to protein adjusted-IC90 (0.064 mg/L) for those above this threshold were 37.9 hours (22.1-63.5) and 108.9 hours [(18.6-129.6); 4.5 days (0.8-5.4); n=13].

CONCLUSIONS

Breastfeeding contributed relatively little to infant plasma exposures but a median of 4.5 days additional prophylaxis to some of the breastfed infants was observed following maternal dolutegravir cessation (3-15 days postpartum), which waned with time postpartum as transplacental dolutegravir cleared.

Evaluation of the Effect of CYP2D6 Genotypes on Tramadol and O-Desmethyltramadol Pharmacokinetic Profiles in a Korean Population Using Physiologically-Based Pharmacokinetic Modeling

Tramadol is a μ-opioid receptor agonist and a monoamine reuptake inhibitor. O-desmethyltramadol (M1), the major active metabolite of tramadol, is produced by CYP2D6. A physiologically-based pharmacokinetic model was developed to predict changes in time-concentration profiles for tramadol and M1 according to dosage and CYP2D6 genotypes in the Korean population. Parallel artificial membrane permeation assay was performed to determine tramadol permeability, and the metabolic clearance of M1 was determined using human liver microsomes. Clinical study data were used to develop the model. Other physicochemical and pharmacokinetic parameters were obtained from the literature. Simulations for plasma concentrations of tramadol and M1 (after 100 mg tramadol was administered five times at 12-h intervals) were based on a total of 1000 virtual healthy Koreans using SimCYP^® simulator. Geometric mean ratios (90% confidence intervals) (predicted/observed) for maximum plasma concentration at steady-state (C_max,ss) and area under the curve at steady-state (AUC_last,ss) were 0.79 (0.69-0.91) and 1.04 (0.85-1.28) for tramadol, and 0.63 (0.51-0.79) and 0.67 (0.54-0.84) for M1, respectively. The predicted time-concentration profiles of tramadol fitted well to observed profiles and those of M1 showed under-prediction. The developed model could be applied to predict concentration-dependent toxicities according to CYP2D6 genotypes and also, CYP2D6-related drug interactions.

Population pharmacokinetic analysis of tramadol and O-desmethyltramadol with genetic polymorphism of CYP2D6

Aim: Tramadol is widely used to treat acute, chronic, and neuropathic pain. Its primary active metabolite, O-desmethyltramadol (M1), is mainly responsible for its µ-opioid receptor-related analgesic effect. Tramadol is metabolized to M1 mainly by the cytochrome P450 (CYP) 2D6 enzyme, and to other metabolites by CYP3A4 and CYP2B6. The aim of this study was to develop a population pharmacokinetic (PK) model of tramadol and its metabolite using healthy Korean subjects.

Methods: Data on plasma concentrations of tramadol and M1 were obtained from 23 healthy Korean male subjects after a twice-daily oral dose of 100 mg of tramadol, every 12 hrs, for a total of 5 times. Blood samples were collected at 0 (pre-dose), 0.5, 1, 1.5, 2, 2.5, 3, 4, 6, 8, 10, 12, 24, 48 and 72 hrs after last administration. Plasma tramadol concentrations were then analyzed using LC/MS. Population PK analysis of tramadol and its metabolite was performed using a nonlinear mixed-effects modeling (NONMEM).

Results: A one-compartment model with combined first-order and zero-order absorption was well fitted to the concentration–time curve of tramadol. M1 was well described by the one-compartment model as an extension of the parent drug (tramadol) model. Genetic polymorphisms of CYP2D6 correlated with the clearance of tramadol, and clearance from the central compartment to the metabolite compartment.

Conclusion: The parent-metabolite model successfully characterized the PK of tramadol and its metabolite M1 in healthy Korean male subjects. These results could be applied to evaluate plasma tramadol concentrations after various dosing regimens.

Physiologically-based pharmacokinetic modelling of infant exposure to efavirenz through breastfeeding

Background: Very little is known about the level of infant exposure to many drugs commonly used during breastfeeding. The aim of this study was to develop a physiologically-based pharmacokinetic (PBPK) model for predicting infant exposure to maternal efavirenz through breastmilk. Methods: A breastfeeding PBPK model combining whole-body maternal and infant sub-models was constructed from drug-specific and system parameters affecting drug disposition using mathematical descriptions. The model was validated against published data on the pharmacokinetics of efavirenz in nursing mother-infant pairs. Further simulations were conducted to assess exposure in the context of the 400 mg reduced dose of efavirenz as well as best- and worse-case scenarios. Results: The model adequately described efavirenz pharmacokinetics, with over 80% of observed data points (203 matched breast milk and plasma pairs) within the predictive interval. All parameters were within 2-fold difference of clinical data. Median (range) predicted versus observed breast milk AUC0-24, Cmax and Cmin at the standard 600 mg dose were 75.0 (18.5-324) versus 68.5 (26.3-257) µg.hr/mL, 4.56 (1.17-16.0) versus 5.39 (1.43-18.4) µg/mL, and 2.11 (0.38-12.3) versus 1.68 (0.316-9.57) µg/mL, respectively. Predicted plasma AUC0-24, Cmax and Cmin at 400 mg reduced dose were similar to clinical data from non-breastfeeding adults. Model-predicted infant plasma concentrations were similar to clinical data, 0.15 (0.026–0.78) μg/mL at the 400 mg maternal dose in pooled analysis, approximately 25% lower than simulated exposure at 600 mg. The maximum exposure index was observed in the youngest infants, 5.9% (2.2-20) at 400 mg and 8.7% (3.2-29) at 600 mg. Thirteen and 36% of 10 days-1 month old infants were predicted to have exposure index above the 10% recommended threshold at 400 mg and 600 mg maternal dose, respectively. Conclusions: This application of PBPK modelling opens up opportunities for expanding our understanding of infant exposure to maternal drugs through breastfeeding.

Drugs in Lactation

One impediment to breastfeeding is the lack of information on the use of many drugs during lactation, especially newer ones. The principles of drug passage into breastmilk are well established, but have often not been optimally applied prospectively. Commonly used preclinical rodent models for determining drug excretion into milk are very unreliable because of marked differences in milk composition and transporters compared to those of humans. Measurement of drug concentrations in humans remains the gold standard, but computer modeling is promising. New FDA labeling requirements present an opportunity to apply modeling to preclinical drug development in place of conventional animal testing for drug excretion into breastmilk, which should improve the use of medications in nursing mothers.

Bioanalytical challenge: A review of environmental and pharmaceuticals contaminants in human milk

An overview of bioanalytical methods for the determination of environmental and pharmaceutical contaminants in human milk is presented. The exposure of children to these contaminants through lactation has been widely investigated. The human milk contains diverse proteins, lipids, and carbohydrates and the concentration of these components is drastically altered during the lactation period providing a high degree of an analytical challenge. Sample collection and pretreatment are still considered the Achilles' heel. This review presents liquid chromatographic methods developed in the last 10 years for this complex matrix with focuses in the extraction and quantification steps. Green sample preparation protocols have been emphasized.

Modeling drug passage into human milk

Breastfeeding has positive health consequences for both the breastfed infant and the nursing mother.(1,2) Although information on drug use during lactation is available through sites such as LactMed,(3) available information is often incomplete. Unlike pregnancy, in which large numbers of pregnant women need to be studied to assure safety, measurement of drug concentrations in breastmilk in a relatively few subjects can provide valuable information to assess drug safety. This article reviews methods of measuring and predicting drug passage into breastmilk.

Pharmacokinetics of Transfer of Azithromycin into the Breast Milk of African Mothers

Azithromycin (AZI) is used for its antibiotic and antimalarial properties in pregnancy. Reported estimates of AZI breast milk transfer, based on concentrations in mostly single samples from small numbers of women, have suggested that infant intake is safe. To better characterize infant intake and the associated potential benefits and risks, AZI was measured by liquid chromatography-mass spectrometry in four breast milk samples taken over 28 days postpartum from each of 20 Gambian women given 2 g AZI during labor. A population pharmacokinetic model utilizing published parameters for AZI disposition in pregnancy, the present breast milk concentrations, and increasing/decreasing sigmoid maximum-effect (Emax) functions adequately described temporal changes in the milk/plasma ratio. The median estimated absolute and relative cumulative infant doses were 4.5 mg/kg of body weight (95% prediction interval, 0.6 to 7.0 mg/kg) and 15.7% (95% prediction interval, 2.0 to 27.8%) of the maternal dose, respectively; the latter exceeded the recommended 10% safety limit. Although some infants with bacterial infections may benefit from AZI in breast milk, there is a risk of hypertrophic pyloric stenosis with a worst-case number needed to harm of 60 based on the present and available epidemiologic data. (This study has been registered at ClinicalTrials.gov under registration no. NCT01800942.).

Population PK modelling and simulation based on fluoxetine and norfluoxetine concentrations in milk: a milk concentration-based prediction model

AIMS

Population pharmacokinetic (pop PK) modelling can be used for PK assessment of drugs in breast milk. However, complex mechanistic modelling of a parent and an active metabolite using both blood and milk samples is challenging. We aimed to develop a simple predictive pop PK model for milk concentration-time profiles of a parent and a metabolite, using data on fluoxetine (FX) and its active metabolite, norfluoxetine (NFX), in milk.

METHODS

Using a previously published data set of drug concentrations in milk from 25 women treated with FX, a pop PK model predictive of milk concentration-time profiles of FX and NFX was developed. Simulation was performed with the model to generate FX and NFX concentration-time profiles in milk of 1000 mothers. This milk concentration-based pop PK model was compared with the previously validated plasma/milk concentration-based pop PK model of FX.

RESULTS

Milk FX and NFX concentration-time profiles were described reasonably well by a one compartment model with a FX-to-NFX conversion coefficient. Median values of the simulated relative infant dose on a weight basis (sRID: weight-adjusted daily doses of FX and NFX through breastmilk to the infant, expressed as a fraction of therapeutic FX daily dose per body weight) were 0.028 for FX and 0.029 for NFX. The FX sRID estimates were consistent with those of the plasma/milk-based pop PK model.

CONCLUSIONS

A predictive pop PK model based on only milk concentrations can be developed for simultaneous estimation of milk concentration-time profiles of a parent (FX) and an active metabolite (NFX).

Pharmacokinetics of piperaquine transfer into the breast milk of Melanesian mothers

Transfer of piperaquine (PQ) into breast milk was examined in 27 Papua New Guinean women given a 3-day course of dihydroartemisinin-PQ or sulfadoxine-pyrimethamine-PQ during the second/third trimester. Breast milk was sampled on days 1, 2, 3 to 5, 7 to 11, and 14 to 17 postdelivery, a median of 70 days postdose (range, 6 to 145 days). A blood sample was taken at delivery, and additional serial samples were available from 9 women who delivered within 42 days of dosing. Milk and plasma PQ were assayed by high-performance liquid chromatography. A population-based approach was used to model the loge(plasma) and milk concentration-time data. A sigmoid Emax model best described PQ breast milk transfer. The population average milk:plasma PQ ratio was 0.58, with a peak of 2.5 at delivery. The model-derived maximum milk intake (148 ml/kg of body weight/day) was similar to the accepted value of 150 ml/kg/day. The median estimated absolute and relative cumulative infant PQ doses were 22 μg and 0.07%, respectively, corresponding to absolute and relative daily doses of 0.41 μg/kg and 0.004%. Model-based simulations for PQ treatment regimens given at birth, 1 week postdelivery, and 6 weeks postdelivery showed that the highest median estimated relative total infant dose (0.36%; median absolute total dose of 101 μg/kg) was seen after maternal PQ treatment 6 weeks postpartum. The maximum simulated relative total and daily doses from any scenario were 4.3% and 2.5%, respectively, which were lower than the recommended 10% upper limit. Piperaquine is transferred into breast milk after maternal treatment doses, but PQ exposure for suckling infants appears safe.

Consensus clinical practice guidelines for the diagnosis and treatment of restless legs syndrome/Willis-Ekbom disease during pregnancy and lactation

Restless legs syndrome (RLS)/Willis-Ekbom disease (WED) is common during pregnancy, affecting approximately one in five pregnant women in Western countries. Many report moderate or severe symptoms and negative impact on sleep. There is very little information in the medical literature for practitioners on the management of this condition during pregnancy. Accordingly, a task force was chosen by the International RLS Study Group (IRLSSG) to develop guidelines for the diagnosis and treatment of RLS/WED during pregnancy and lactation. A committee of nine experts in RLS/WED and/or obstetrics developed a set of 12 consensus questions, conducted a literature search, and extensively discussed potential guidelines. Recommendations were approved by the IRLSSG executive committee, reviewed by IRLSSG membership, and approved by the WED Foundation Medical Advisory Board. These guidelines address diagnosis, differential diagnosis, clinical course, and severity assessment of RLS/WED during pregnancy and lactation. Nonpharmacologic approaches, including reassurance, exercise and avoidance of exacerbating factors, are outlined. A rationale for iron supplementation is presented. Medications for RLS/WED are risk/benefit rated for use during pregnancy and lactation. A few are rated "may be considered" when RLS/WED is refractory to more conservative approaches. An algorithm summarizes the recommendations. These guidelines are intended to improve clinical practice and promote further research.

Mathematical model approach to describe tumour response in mice after vaccine administration and its applicability to immune-stimulatory cytokine-based strategies

Immunotherapy is a growing therapeutic strategy in oncology based on the stimulation of innate and adaptive immune systems to induce the death of tumour cells. In this paper, we have developed a population semi-mechanistic model able to characterize the mechanisms implied in tumour growth dynamic after the administration of CyaA-E7, a vaccine able to target antigen to dendritic cells, thus triggering a potent immune response. The mathematical model developed presented the following main components: (1) tumour progression in the animals without treatment was described with a linear model, (2) vaccine effects were modelled assuming that vaccine triggers a non-instantaneous immune response inducing cell death. Delayed response was described with a series of two transit compartments, (3) a resistance effect decreasing vaccine efficiency was also incorporated through a regulator compartment dependent upon tumour size, and (4) a mixture model at the level of the elimination of the induced signal vaccine (k 2) to model tumour relapse after treatment, observed in a small percentage of animals (15.6%). The proposed model structure was successfully applied to describe antitumor effect of IL-12, suggesting its applicability to different immune-stimulatory therapies. In addition, a simulation exercise to evaluate in silico the impact on tumour size of possible combination therapies has been shown. This type of mathematical approaches may be helpful to maximize the information obtained from experiments in mice, reducing the number of animals and the cost of developing new antitumor immunotherapies.

Pharmacogenomic considerations in opioid analgesia

Translating pharmacogenetics to clinical practice has been particularly challenging in the context of pain, due to the complexity of this multifaceted phenotype and the overall subjective nature of pain perception and response to analgesia. Overall, numerous genes involved with the pharmacokinetics and dynamics of opioids response are candidate genes in the context of opioid analgesia. The clinical relevance of CYP2D6 genotyping to predict analgesic outcomes is still relatively unknown; the two extremes in CYP2D6 genotype (ultrarapid and poor metabolism) seem to predict pain response and/or adverse effects. Overall, the level of evidence linking genetic variability (CYP2D6 and CYP3A4) to oxycodone response and phenotype (altered biotransformation of oxycodone into oxymorphone and overall clearance of oxycodone and oxymorphone) is strong; however, there has been no randomized clinical trial on the benefits of genetic testing prior to oxycodone therapy. On the other hand, predicting the analgesic response to morphine based on pharmacogenetic testing is more complex; though there was hope that simple genetic testing would allow tailoring morphine doses to provide optimal analgesia, this is unlikely to occur. A variety of polymorphisms clearly influence pain perception and behavior in response to pain. However, the response to analgesics also differs depending on the pain modality and the potential for repeated noxious stimuli, the opioid prescribed, and even its route of administration.

Tramadol in pregnancy and lactation

Tramadol produces analgesic effects through both non-opioid and weak opioid activity and is commonly used to treat mild to moderate pain. It has been in use for over 30 years and has a well-established safety profile in the general population. Since tramadol is not licensed for use in pregnancy and lactation, there is limited clinical research on its use in this patient population. A systematic review was undertaken of articles published in English before June 2011, searching Pubmed, Medline, CINAHL, Embase and Cochrane databases using the terms 'tramadol and pregnancy', 'tramadol and breastfeeding', 'tramadol and lactation', and 'tramadol and neonate'.