Derivatization-free determination of short-chain volatile amines in human plasma and urine by headspace gas chromatography-mass spectrometry.

BACKGROUND: Short-chain volatile amines (SCVA) are an interesting compound class playing crucial roles in physiological and toxicological human settings. Dimethylamine (DMA), trimethylamine (TMA), diethylamine (DEA), and triethylamine (TEA) were investigated in detail. METHODS: Headspace gas chromatography coupled to mass spectrometry (HS-GC-MS) was used for the simultaneous qualitative and quantitative determination of four SCVA in different human body fluids. Four hundred microliters of Li-heparin plasma and urine were analyzed after liberation of volatile amines under heated conditions in an aqueous alkaline and saline environment. Target analytes were separated on a volatile amine column and detected on a Thermo DSQ II mass spectrometer scheduled in single ion monitoring mode. RESULTS: Chromatographic separation of selected SCVA was done within 7.5 minutes. The method was developed and validated with respect to accuracy, precision, recovery and stability. Accuracy and precision criteria were below 12% for all target analytes at low and high levels. The selected extraction procedure provided recoveries of more than 92% from both matrices for TMA, DEA and TEA. The recovery of DMA from Li-heparin plasma was lower but still in the acceptable range (>75%). The newly validated method was successfully applied to plasma and urine samples from healthy volunteers. Detected concentrations of endogenous metabolites DMA and TMA are comparable to already known reference ranges. CONCLUSION: Herein, we describe the successful development and validation of a reliable and broadly applicable HS-GC-MS procedure for the simultaneous and quantitative determination of SCVA in human plasma and urine without relying on derivatization chemistry.

Quantification of hydrolyzed peptides and proteins by amino acid fluorescence.

Reliable quantification of peptides and proteins is essential for drug discovery. We report the successful development and validation of an accurate and broadly applicable high performance liquid chromatography hyphenated to fluorescence detector procedure for the quantitative determination of the aromatic amino acids tyrosine, phenylalanine, and tryptophan, without relying on derivatization chemistry. Using ion-pair chromatography, fluorescent amino acids were clearly separated within 10 minutes. The hydrolysis of peptides was performed under acidic and heated conditions to yield the monomeric building blocks. Various protecting agents were tested to ensure tryptophan stability. The presented analytical method accurately (>95%) quantifies all fluorescent residues. The power of the method was confirmed by correct quantification of protein reference standard to 98.6% over all fluorescence traces. The method allowed us to identify pre-analytical differences between the nominal and actual concentrations of 12 peptide solutions. Salt formation, weighing errors, and other pre-analytical pitfalls resulted in noteworthy differences of up to 85% between the indicated and actual concentration of peptide solutions, subsequently leading to false positive or negative interpretation of activity data. Finally, only one solution is needed to perform quantification as well as UV-purity tests and can further be used as stock solution for activity testing.

Generative Recurrent Networks for De Novo Drug Design.

Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical space and focus on regions of interest. We present a method for molecular de novo design that utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells. This computational model captured the syntax of molecular representation in terms of SMILES strings with close to perfect accuracy. The learned pattern probabilities can be used for de novo SMILES generation. This molecular design concept eliminates the need for virtual compound library enumeration. By employing transfer learning, we fine-tuned the RNN's predictions for specific molecular targets. This approach enables virtual compound design without requiring secondary or external activity prediction, which could introduce error or unwanted bias. The results obtained advocate this generative RNN-LSTM system for high-impact use cases, such as low-data drug discovery, fragment based molecular design, and hit-to-lead optimization for diverse drug targets.