In vitro and in vivo evaluation of O-alkyl derivatives of tramadol

Structural elucidation of tramadol, its derivatives, and metabolites using chemical derivatization and liquid chromatography-high-resolution tandem mass spectrometry

RATIONALE

Tramadol (T) is a strong painkiller drug that belongs to the opioid analgesic group. Several accidental intoxication cases after oral administration of T have been reported in the past decade. Tramadol, its derivatives, and metabolites present information-limited mass spectra with one prominent peak representing the amine-containing residue; therefore, their structural determination based on both electron impact mass spectrometry (EI-MS) and ESI-MS/MS spectra could be misleading.

METHODS

A novel analytical method for the structural elucidation of tramadol, its four homologs, and its two main phase I metabolites (N-desmethyltramadol and O-desmethyltramadol) was developed using chemical modification and liquid chromatography-high-resolution tandem mass spectrometry (LC-HR-MS/MS) with Orbitrap technology.

RESULTS

After chemical derivatization, each of the investigated T series exhibited informative mass spectra that enabled better exposition of their structures. The developed method was successfully implemented to explicitly identify the structures of tramadol and its N-desmethyltramadol metabolite in urine samples at low ng/mL levels.

CONCLUSIONS

An efficient derivatization-aided strategy was developed for rapidly elucidating the structure of tramadol-like compounds. The method is intended to assist forensic chemists in better diagnosing T and its analogs and metabolites in clinical or forensic toxicology laboratories.

Molecular Network-Based Identification of Tramadol Metabolites in a Fatal Tramadol Poisoning

Identification of xenobiotics and their phase I/II metabolites in poisoned patients remains challenging. Systematic approaches using bioinformatic tools are needed to detect all compounds as exhaustively as possible. Here, we aimed to assess an analytical workflow using liquid chromatography coupled to high-resolution mass spectrometry with data processing based on a molecular network to identify tramadol metabolites in urine and plasma in poisoned patients. The generated molecular network from liquid chromatography coupled to high-resolution tandem mass spectrometry data acquired in both positive and negative ion modes allowed for the identification of 25 tramadol metabolites in urine and plasma, including four methylated metabolites that have not been previously reported in humans or in vitro models. While positive ion mode is reliable for generating a network of tramadol metabolites displaying a dimethylamino radical in their structure, negative ion mode was useful to cluster phase II metabolites. In conclusion, the combined use of molecular networks in positive and negative ion modes is a suitable and robust tool to identify a broad range of metabolites in poisoned patients, as shown in a fatal tramadol-poisoned patient.

New pharmacological perspectives and therapeutic options for opioids: Differences matter

Opioids remain the major drug class for the treatment of acute, chronic and cancer pain, but have major harmful effects such as dependence and opioid-induced ventilatory impairment. Although no new typical opioids have come onto the market in the past almost 50 years, a plethora of new innovative formulations has been developed to meet the clinical need. This review is intended to shed light on new understanding of the molecular pharmacology of opioids, which has arisen largely due to the genomic revolution, and what new drugs may become available in the coming years. Atypical opioids have and are being developed which not only target the mu opioid receptor but other targets in the pain pathway. Biased mu agonists have been developed but remain 'unbiased' clinically. The contribution of drugs targeting non-mu opioid receptors either alone or as heterodimers shows potential promise but remains understudied. That gene splice variants of the mu opioid receptor produce multiple receptor isoforms in different brain regions, and may change with pain chronicity and phenotype, presents new challenges but also opportunities for precision pain medicine. Finally, that opioids also have pro-inflammatory effects not aligned with mu opioid receptor binding affinity implicates a fresh understanding of their role in chronic pain, whether cancer or non-cancer. Hopefully, a new understanding of opioid analgesic drug action may lead to new drug development and better precision medicine in acute and chronic pain relief with less patient harm.

Synthesis of C-prenylated analogues of stilbenoid methyl ethers and their cyclic dihydrobenzopyranyl derivatives as potential anti-inflammatory agents

An efficient and versatile synthesis of the naturally occurring C-prenylated stilbenoid methyl ethers and synthetic analogs has been developed.

Physiology-based IVIVE predictions of tramadol from in vitro metabolism data

PURPOSE

To predict the tramadol in vivo pharmacokinetics in adults by using in vitro metabolism data and an in vitro-in vivo extrapolation (IVIVE)-linked physiologically-based pharmacokinetic (PBPK) modeling and simulation approach (Simcyp®).

METHODS

Tramadol metabolism data was gathered using metabolite formation in human liver microsomes (HLM) and recombinant enzyme systems (rCYP). Hepatic intrinsic clearance (CLintH) was (i) estimated from HLM corrected for specific CYP450 contributions from a chemical inhibition assay (model 1); (ii) obtained in rCYP and corrected for specific CYP450 contributions by study-specific intersystem extrapolation factor (ISEF) values (model 2); and (iii) scaled back from in vivo observed clearance values (model 3). The model-predicted clearances of these three models were evaluated against observed clearance values in terms of relative difference of their geometric means, the fold difference of their coefficients of variation, and relative CYP2D6 contribution.

RESULTS

Model 1 underpredicted, while model 2 overpredicted the total tramadol clearance by -27 and +22%, respectively. The CYP2D6 contribution was underestimated in both models 1 and 2. Also, the variability on the clearance of those models was slightly underpredicted. Additionally, blood-to-plasma ratio and hepatic uptake factor were identified as most influential factors in the prediction of the hepatic clearance using a sensitivity analysis.

CONCLUSION

IVIVE-PBPK proved to be a useful tool in combining tramadol's low turnover in vitro metabolism data with system-specific physiological information to come up with reliable PK predictions in adults.