Innovative analytical methods for Central Nervous System Drug analysis in biological fluids

During recent years a consistent number of central nervous system (CNS) drugs have been approved and introduced on the market for the treatment of many psychiatric and neurological disorders, including psychosis, depression, Parkinson disease and epilepsy. Despite the great advancements obtained in the treatment of CNS diseases/disorders, partial response to therapy or treatment failure are frequent, at least in part due to poor compliance, but also genetic variability in the metabolism of psychotropic agents or polypharmacy, which may lead to sub-therapeutic or toxic plasma levels of the drugs, and finally inefficacy of the treatment or adverse/toxic effects. With the aim of improving the treatment, reducing toxic/side effects and patient hospitalisation, Therapeutic Drug Monitoring (TDM) is certainly useful, allowing for a personalisation of the therapy. Reliable analytical methods are required to determine the plasma levels of psychotropic drugs, which are often present at low concentrations (tens or hundreds of nanograms per millilitre). The present PhD Thesis has focused on the development of analytical methods for the determination of CNS drugs in biological fluids, including antidepressants (sertraline and duloxetine), antipsychotics (aripiprazole), antiepileptics (vigabatrin and topiramate) and antiparkinsons (pramipexole). Innovative methods based on liquid chromatography or capillary electrophoresis coupled to diode-array or laser-induced fluorescence detectors have been developed, together with the suitable sample pre-treatment for interference removal and fluorescent labelling in case of LIF detection. All methods have been validated according to official guidelines and applied to the analysis of real samples obtained from patients, resulting suitable for the TDM of psychotropic drugs.

Drug of abuse analysis: new methods in herbal and biological matrices for medicinal chemistry and forensic investigation

In this Ph.D. project, original and innovative approaches for the quali-quantitative analysis of abuse substances, as well as therapeutic agents with abuse potential and related compounds were designed, developed and validated for application to different fields such as forensics, clinical and pharmaceutical. All the parameters involved in the developed analytical workflows were properly and accurately optimised, from sample collection to sample pretreatment up to the instrumental analysis. Advanced dried blood microsampling technologies have been developed, able of bringing several advantages to the method as a whole, such as significant reduction of solvent use, feasible storage and transportation conditions and enhancement of analyte stability. At the same time, the use of capillary blood allows to increase subject compliance and overall method applicability by exploiting such innovative technologies. Both biological and non-biological samples involved in this project were subjected to optimised pretreatment techniques developed ad-hoc for each target analyte, making also use of advanced microextraction techniques. Finally, original and advanced instrumental analytical methods have been developed based on high and ultra-high performance liquid chromatography (HPLC,UHPLC) coupled to different detection means (mainly mass spectrometry, but also electrochemical, and spectrophotometric detection for screening purpose), and on attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) for solid-state analysis. Each method has been designed to obtain highly selective, sensitive yet sustainable systems and has been validated according to international guidelines. All the methods developed herein proved to be suitable for the analysis of the compounds under investigation and may be useful tools in medicinal chemistry, pharmaceutical analysis, within clinical studies and forensic investigations.

Anticancer drug discovery using artificial intelligence: an application in pharmacological activity prediction

Hematological cancers are a heterogeneous family of diseases that can be divided into leukemias, lymphomas, and myelomas, often called “liquid tumors”. Since they cannot be surgically removable, chemotherapy represents the mainstay of their treatment. However, it still faces several challenges like drug resistance and low response rate, and the need for new anticancer agents is compelling. The drug discovery process is long-term, costly, and prone to high failure rates. With the rapid expansion of biological and chemical "big data", some computational techniques such as machine learning tools have been increasingly employed to speed up and economize the whole process. Machine learning algorithms can create complex models with the aim to determine the biological activity of compounds against several targets, based on their chemical properties. These models are defined as multi-target Quantitative Structure-Activity Relationship (mt-QSAR) and can be used to virtually screen small and large chemical libraries for the identification of new molecules with anticancer activity. The aim of my Ph.D. project was to employ machine learning techniques to build an mt-QSAR classification model for the prediction of cytotoxic drugs simultaneously active against 43 hematological cancer cell lines. For this purpose, first, I constructed a large and diversified dataset of molecules extracted from the ChEMBL database. Then, I compared the performance of different ML classification algorithms, until Random Forest was identified as the one returning the best predictions. Finally, I used different approaches to maximize the performance of the model, which achieved an accuracy of 88% by correctly classifying 93% of inactive molecules and 72% of active molecules in a validation set. This model was further applied to the virtual screening of a small dataset of molecules tested in our laboratory, where it showed 100% accuracy in correctly classifying all molecules. This result is confirmed by our previous in vitro experiments.

Study of the urinary biomarkers of synthetic cannabinoid receptor agonists (SCRAs) for the forensic unequivocal proof of consumption: medico-legal implications for drug abuse prevention

Introduction. Synthetic cannabinoid receptor agonists (SCRAs) represent the widest group of New Psychoactive Substances (NPS) and, around 2021-2022, new compounds emerged on the market. The aims of the present research were to identify suitable urinary markers of Cumyl-CB-MEGACLONE, Cumyl-NB-MEGACLONE, Cumyl-NB-MINACA, 5F-EDMB-PICA, EDMB-PINACA and ADB-HEXINACA, to present data on their prevalence and to adapt the methodology from the University of Freiburg to the University of Bologna. Materials and methods. Human phase-I metabolites detected in 46 authentic urine samples were confirmed in vitro with pooled human liver microsomes (pHLM) assays, analyzed by liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-qToF-MS). Prevalence data were obtained from urines collected for abstinence control programs. The method to study SCRAs metabolism in use at the University of Freiburg was adapted to the local facilities, tested in vitro with 5F-EDMB-PICA and applied to the study of ADB-HEXINACA metabolism. Results. Metabolites built by mono, di- and tri-hydroxylation were recommended as specific urinary biomarkers to monitor the consumption of SCRAs bearing a cumyl moiety. Monohydroxylated and defluorinated metabolites were suitable proof of 5F-EDMB-PICA consumption. Products of monohydroxylation and amide or ester hydrolysis, coupled to monohydroxylation or ketone formation, were recognized as specific markers for EDMB-PINACA and ADB-HEXINACA. The LC-qToF-MS method was successfully adapted to the University of Bologna, as tested with 5F-EDMB-PICA in vitro metabolites. Prevalence data showed that 5F-EDMB-PINACA and EDMB-PINACA were more prevalent than ADB-HEXINACA, but for a limited period. Conclusion. Due to undetectability of parent compounds in urines and to shared metabolites among structurally related compounds, the identification of specific urinary biomarkers as unequivocal proofs of SCRAs consumption remains challenging for forensic laboratories. Urinary biomarkers are necessary to monitor SCRAs abuse and prevalence data could help in establishing tailored strategies to prevent their spreading, highlighting the role for legal medicine as a service to public health.