Lipophilicity, plasma protein binding and intrinsic clearance of fentanyl analogues
Bibliographic record
Abstract
Introduction: Fentanyl analogues have infiltrated the illicit drug market within the USA, causing grave concerns for public health and law enforcement agencies. These synthetic opioids, derived from fentanyl, have gained popularity among drug traffickers due to their high potency and potential for profit. The introduction of fentanyl and fentanyl analogues to the USA and Canadian illicit opiate supply is thought to play a significant role in the increase in synthetic opioid related deaths, which rose 18% in 2021. Despite widespread concerns surrounding their use, limited pharmacological data for these new analogues is available. Objectives: This research aims to identify and investigate the lipophilicity, plasma protein binding (PPB), and intrinsic clearance of fentanyl and 14 fentanyl analogues (4-fluorobutyrfentanyl, 4-fluoroisobutyrfentanyl, acetylfentanyl, acrylfentanyl, butyrfentanyl, carfentanil, crotonylfentanyl, cyclopropylfentanyl, furanylfentanyl, methoxyacetylfentanyl, ocfentanil, ortho-fluorofentanyl, para-fluorofentanyl and valerylfentanyl). Methods: LogD (pH 7.4) was determined using a HPLC-PDA-based chromatographic hydrophobicity index (CHI) method. A calibration mix of ten reference compounds was analysed at the start and end of each test run, with test compounds injected in triplicate. Average retention times of calibration compounds were used to calculate retention factors (k), which were then plotted against literature CHI values (CHI0 ). Experimental CHI and CHI logD values were subsequently calculated. PPB was determined by equilibrium dialysis. Pooled human plasma was centrifuged (3750 rpm, 10 min, 22 °C) and spiked with test compounds or positive control (nicardipine) (3 µg mL-1). Equilibrated, spiked plasma was dialysed against isotonic phosphate buffer (pH 7.4) (5 h, 37 °C, 100 rpm). ACN containing donepezil internal standard (10 ng mL-1) was added to each sample which were then centrifuged (3750 rpm, 10 min, 22 °C). The supernatant was diluted in dH2 O prior to UPLC-MS/MS analysis. The percentage of drug bound, and fraction unbound were calculated. For intrinsic clearance incubations, test compounds and a positive control (verapamil) were incubated at 0.5 µM in HLM (0.5 mg microsomal protein mL -1 in 50 mM phosphate buffer, pH 7.4). NADPH in phosphate buffer (final concentration 0.8 mg mL-1) was used to initiate the reaction (500 µL total incubation volume). Plates were incubated (37 °C, 100 rpm) and samples were collected from 0 to 60 min and quenched in ACN containing donepezil (10 ng mL-1). Samples were diluted with dH2 O and centrifuged (3750 rpm, 10 min, 22 °C) prior to UPLC-MS/MS analysis. Rate constants (k, min-1), half-lives (t1/2 , min), microsomal intrinsic clearance (CLint micr , mL min-1 mg-1) and intrinsic clearance (CLint , mL min-1 kg-1) rates were calculated. In vivo hepatic clearance (CLH , mL min-1 kg-1) was estimated according to the ‘well-stirred’ model. Results: LogD7.4 results ranged from 3.42 (methoxyacetylfentanyl) to 6.11 (valerylfentanyl). PPB ranged from 96.8% (valerylfentanyl) to 31.6% (acetylfentanyl). In vitro t1/2 ranged from 7.5 (furanylfentanyl) to 53.0 mins (methoxyacetylfentanyl). This resulted in CLint rates ranging from 250 (furanylfentanyl) to 35.4 (methoxyacetlfentanyl) mL min-1 kg-1. CLH was estimated to range from 16.84 (acetylfentanyl) to 2.64 (cyclopropylfentanyl) mL min-1 kg-1. Conclusion/ Discussion: The effect of various structural changes between fentanyl analogues on the tested properties were established. For example, increasing alkyl chain length (i.e. acetylfentanyl > fentanyl > butyrfentanyl > valerylfentanyl) resulted in increased lipophilicity, increased PPB, and an overall slower rate of predicted hepatic clearance. Fluorination (fentanyl vs ortho-fluorofentanyl/ para-fluorofentanyl; methoxyacetylfentanyl vs ocfentanil; butyrfentanyl vs 4-fluorobutyrfentanyl) resulted in faster CLint but otherwise had little effect. Differences in drug properties, often unknown to users, will result in unpredictable clinical effects increasing overdose risk and complicating treatment. The extended duration of whole-body clearance, in comparison to fentanyl, could potentially affect the effectiveness of overdose treatments e.g. naloxone. Its short half-life, coupled with these longer clearing opioids, may increase the risk of relapse.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".