Development of a linear dual column HPLC-MS/MS method and clinical genetic evaluation for tramadol and its phase I and II metabolites in oral fluid

First derivative synchronous spectrofluorimetric method for the simultaneous determination of tramadol and celecoxib in their dosage forms and human plasma

One of the most common features of many different clinical conditions is pain; hence, there is a crucial need for eliminating or reducing it to a tolerable level to retrieve physical, psychological and social functioning. A first derivative synchronous spectrofluorimetry technique is proposed for the simultaneous determination of celecoxib and tramadol HCl, a recent coformulation authorized for treating acute pain in adults. The method includes using synchronous spectrofluorimetry at ∆λ = 80 nm where tramadol HCl was determined using first derivative technique at λ = 230.2 nm, while celecoxib was determined at λ = 288.24 nm. The proposed method was successfully applied to their co-formulated dosage forms in addition to spiked human plasma and validated in agreement with the guidelines of the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH). The linear ranges were found to be 0.50-5.0 and 0.15-0.50, the limits of detection to be 0.088 and 0.011 and the limits of quantification to be 0.266 and 0.032 μg/ml for celecoxib and tramadol, respectively. Statistical analysis revealed no significant difference when compared with previously reported methods as evidenced by the values of the variance ratio F-test and Student t-test. The proposed method was successfully applied to commercial dosage forms and spiked human samples. Moreover, the greenness of the proposed method was investigated based on the analytical eco-scale approach, with the results showing an excellent green scale with a score of 95.

Eco-friendly simultaneous multi-spectrophotometric estimation of the newly approved drug combination of celecoxib and tramadol hydrochloride tablets in its dosage form

Food and Drug Administration (FDA) recently approved co-formulated celecoxib and tramadol for the treatment of acute pain in adults. Three spectrophotometric methods were efficiently applied to estimate the co-formulated Celecoxib and Tramadol in their tablets; second derivative 2D-spectrophotometry technique (method I), induced dual-wavelength technique (method II) and dual-wavelength resolution technique (method III). The proposed methods were successfully validated following the International Council for Harmonisation (ICH) guidelines and statistically assessed based on the correlation coefficients, relative standard deviations as well as detection and quantitation limits. The obtained results revealed non-significant differences compared to the reported results as revealed by the variance ratio F test and Student t test. Moreover, the applied techniques were further assessed concerning their greenness based on the analytical eco-scale method revealing an excellent green scale with a final score of 95. The proposed spectrophotometric techniques could be applied for the routine analysis and quality control of the studied drugs in their dosage form.

Pharmacogenetic Guided Opioid Therapy Improves Chronic Pain Outcomes and Comorbid Mental Health: A Randomized, Double-Blind, Controlled Study

Interindividual variability in analgesic response is at least partly due to well-characterized polymorphisms that are associated with opioid dosing and adverse outcomes. The Clinical Pharmacogenetics Implementation Consortium (CPIC) has put forward recommendations for the CYP2D6 phenotype, but the list of studied drug-gene pairs continues to grow. This clinical trial randomized chronic pain patients (n = 60), referred from primary care to pain unit care into two opioid prescribing arms, one guided by CYP2D6, μ-opioid receptor (OPRM1), and catechol-O-methyl transferase (COMT) genotypes vs. one with clinical routine. The genotype-guided treatment reduced pain intensity (76 vs. 59 mm, p < 0.01) by improving pain relief (28 vs. 48 mm, p < 0.05), increased quality of life (43 vs. 56 mm p < 0.001), and lowered the incidence of clinically relevant adverse events (3 [1-5] vs. 1 [0-2], p < 0.01) and 42% opioid dose (35 [22-61] vs. 60 [40-80] mg/day, p < 0.05) as opposed to usual prescribing arm. The final health utility score was significantly higher (0.71 [0.58-0.82] vs. 0.51 [0.13-0.67] controls, p < 0.05) by improving sleepiness and depression comorbidity, with a significant reduction of 30-34% for headache, dry mouth, nervousness, and constipation. A large-scale implementation analysis could help clinical translation, together with a pharmaco-economic evaluation.

Application of oxidized multi-walled carbon nanotubes and zeolite nanoparticles for simultaneous preconcentration of codeine and tramadol in saliva prior to HPLC determination

In this work, a dispersive micro-solid phase extraction technique along with high-performance liquid chromatography-UV detection was developed for simultaneous preconcentraion and determination of trace levels of codeine and tramadol in human saliva. This method is based on the adsorption of codeine and tramadol on a mixture of oxidized multi-walled carbon nanotubes and zeolite Y nanoparticles with 1:1 ratio as an efficient nanosorbent. Various analytical parameters influencing the adsorption step including the amount of adsorbent, the pH of the sample solution, the temperature, the stirring rate, the contact time of the sample solution, and the adsorption capacity were investigated. Based on the results, 10 mg adsorbent, sample solutions with pH = 7.6, temperature of 25 °C, stirring rate 750 rpm and contact time 15 min, in the adsorption step shows the best results for both drugs. Then the effective parameters on the analyte desorption stage such as the type of desorption solution, pH of the desorption solution, desorption time and desorption volume were investigated. Studies have shown that water/methanol (50:50 v/v) as desorption solution, pH = 2.0, desorption time of 5 min and desorption volume of 2 ml gives the best results.Chromatographic separation was performed on a RP-Shim-pack CLC-ODS-C18 column (250 mm × 4.6 mm, 5 µm) with isocratic mode. The mobile phase contained of acetonitrile:phosphate buffer (18:82, v/v) at pH = 4.5 and the flow rate was 1 ml.min^-1. The wavelength of UV detector was adjusted at 210 nm and 198 nm for codeine and tramadol, respectively.Under optimum conditions, the extraction efficiencies of 98.5% and 99.2% were achieved for codeine and tramadol respectively. Enrichment factor of 13, detection limit of 0.3 μg L^-1, relative standard deviation of 4.07 for codeine; and an enrichment factor of 15, a detection limit of 0.15 μg L^-1, and standard deviation of 2.06 for tramadol were calculated. The linear range of the procedure for each drug was 1.0 to 1000 μg L^-1. This method was successfully applied for the analysis of codeine and tramadol in saliva samples.

CYP2D6 phenotypes and opioid metabolism: the path to personalized analgesia

INTRODUCTION

Opioids play a fundamental role in chronic pain, especially considering when 1 of 5 Europeans adults, even more in older females, suffer from it. However, half of them do not reach an adequate pain relief. Could pharmacogenomics help to choose the most appropriate analgesic drug?

AREAS COVERED

The objective of the present narrative review was to assess the influence of cytochrome P450 2D6 (CYP2D6) phenotypes on pain relief, analgesic tolerability, and potential opioid misuse. Until December 2021, a literature search was conducted through the MEDLINE, PubMed database, including papers from the last 10 years. CYP2D6 plays a major role in metabolism that directly impacts on opioid (tramadol, codeine, or oxycodone) concentration with differences between sexes, with a female trend toward poorer pain control. In fact, CYP2D6 gene variants are the most actionable to be translated into clinical practice according to regulatory drug agencies and international guidelines.

EXPERT OPINION

CYP2D6 genotype can influence opioids' pharmacokinetics, effectiveness, side effects, and average opioid dose. This knowledge needs to be incorporated in pain management. Environmental factors, psychological together with genetic factors, under a sex perspective, must be considered when you are selecting the most personalized pain therapy for your patients.

Plasma metabolites changes in male heroin addicts during acute and protracted withdrawal

BACKGROUND

Heroin addiction and withdrawal have been associated with an increased risk for infectious diseases and psychological complications. However, the changes of metabolites in heroin addicts during withdrawal remain largely unknown.

METHODS

A total of 50 participants including 20 heroin addicts with acute abstinence stage, 15 with protracted abstinence stage and 15 healthy controls, were recruited. We performed metabolic profiling of plasma samples based on ultraperformance liquid chromatography coupled to tandem mass spectrometry to explore the potential biomarkers and mechanisms of heroin withdrawal.

RESULTS

Among the metabolites analyzed, omega-6 polyunsaturated fatty acids (linoleic acid, dihomo-gamma-linolenic acid, arachidonic acid, n-6 docosapentaenoic acid), omega-3 polyunsaturated fatty acids (docosahexaenoic acid, docosapentaenoic acid), aromatic amino acids (phenylalanine, tyrosine, tryptophan), and intermediates of the tricarboxylic acid cycle (oxoglutaric acid, isocitric acid) were significantly reduced during acute heroin withdrawal. Although majority of the metabolite changes could recover after months of withdrawal, the levels of alpha-aminobutyric acid, alloisoleucine, ketoleucine, and oxalic acid do not recover.

CONCLUSIONS

In conclusion, the plasma metabolites undergo tremendous changes during heroin withdrawal. Through metabolomic analysis, we have identified links between a framework of metabolic perturbations and withdrawal stages in heroin addicts.

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.

Integrated Non-targeted and Targeted Metabolomics Uncovers Dynamic Metabolic Effects during Short-Term Abstinence in Methamphetamine Self-Administering Rats

Persistent neurochemical disturbances by repeating drug reward and withdrawal lead to addiction. Particularly, drug withdrawal, usually starting within hours of the last dose, is considered as a critical step in the transition to addiction and a treatment clue. The aim of this study was to uncover metabolic effects associated with methamphetamine (MA) short-term abstinence using both non-targeted and targeted metabolomics. Metabolic alterations were investigated in rat plasma collected immediately after 16 days of MA self-administration and after 12 and 24 h of abstinence. Principal component analysis revealed that the highest level of separation occurred between the 24 h and saline (control) groups based on the significantly changed ion features, 257/320/333 and 331/409/388, in the SA/12 h/24 h groups in positive and negative modes of UPLC-QTOF-ESI-MS, respectively. Targeted metabolomics revealed dynamic changes in the biosynthesis/metabolism of amino acids, including the phenylalanine, tyrosine, and tryptophan biosynthesis and the valine, leucine, and isoleucine biosynthesis. Integrating non-targeted and targeted metabolomics data uncovered rapid and distinct changes in the metabolic pathways involved in energy metabolism, the nervous system, and membrane lipid metabolism. These findings provide essential knowledge of the dynamic metabolic effects associated with short-term MA abstinence and may help identify early warning signs of MA dependence.

Current Understanding of Methamphetamine-Associated Metabolic Changes Revealed by the Metabolomics Approach

Metabolomics is a powerful tool used in the description of metabolic system perturbations caused by diseases or abnormal conditions, and it usually involves qualitative and/or quantitative metabolome determination, accompanied by bioinformatics assessment. Methamphetamine is a psychostimulant with serious abuse potential and due to the absence of effective pharmacotherapy and a high recurrence potential, methamphetamine addiction is a grave issue. Moreover, its addiction mechanisms remain unclear, probably due to the lack of experimental models that reflect personal genetic variances and environmental factors determining drug addiction occurrence. The metabolic approach is only recently being used to study the metabolic effects induced by a variety of methamphetamine exposure statuses, in order to investigate metabolic disturbances related to the adverse effects and discover potential methamphetamine addiction biomarkers. To provide a critical overview of methamphetamine-associated metabolic changes revealed in recent years using the metabolomics approach, we discussed methamphetamine toxicity, applications of metabolomics in drug abuse and addiction studies, biological samples used in metabolomics, and previous studies on metabolic alterations in a variety of biological samples—including the brain, hair, serum, plasma, and urine—following methamphetamine exposure in animal studies. Metabolic alterations observed in animal brain and other biological samples after methamphetamine exposure were associated with neuronal and energy metabolism disruptions. This review highlights the significance of further metabolomics studies in the area of methamphetamine addiction research. These findings will contribute to a better understanding of metabolic changes induced by methamphetamine addiction progress and to the design of further studies targeting the discovery of methamphetamine addiction biomarkers and therapeutic targets.

Oral Fluid Drug Testing: Analytical Approaches, Issues and Interpretation of Results

With advances in analytical technology and new research informing result interpretation, oral fluid (OF) testing has gained acceptance over the past decades as an alternative biological matrix for detecting drugs in forensic and clinical settings. OF testing offers simple, rapid, non-invasive, observed specimen collection. This article offers a review of the scientific literature covering analytical methods and interpretation published over the past two decades for amphetamines, cannabis, cocaine, opioids, and benzodiazepines. Several analytical methods have been published for individual drug classes and, increasingly, for multiple drug classes. The method of OF collection can have a significant impact on the resultant drug concentration. Drug concentrations for amphetamines, cannabis, cocaine, opioids, and benzodiazepines are reviewed in the context of the dosing condition and the collection method. Time of last detection is evaluated against several agencies' cutoffs, including the proposed Substance Abuse and Mental Health Services Administration, European Workplace Drug Testing Society and Driving Under the Influence of Drugs, Alcohol and Medicines cutoffs. A significant correlation was frequently observed between matrices (i.e., between OF and plasma or blood concentrations); however, high intra-subject and inter-subject variability precludes prediction of blood concentrations from OF concentrations. This article will assist individuals in understanding the relative merits and limitations of various methods of OF collection, analysis and interpretation.

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.