dataesr/scientific-paragraphs-categorization huggingface dataset:
Progress in artificial intelligence (AI) driven predictive approaches heavily relies on the availability of good quality, well-curated, reusable data and capturing biotransformation pathways and metabolite information in a structurally searchable manner is not trivial. Implementing customized corporate biotransformation databases is crucial in maximizing the value that can be harvested from biotransformation studies carried out over the many years in a pharmaceutical company. (Iegre et al. , 2016).
ACKNOWLEDGEMENTS This mini-review is prepared as part of the special issue to celebrate  Prof. F. Peter Guengerich 's scientific contributions to the field as a recipient of the  Bernard B. Brodie Award  in  Drug Metabolism  and Disposition as well as a part of 50 th anniversary celebration of DMD in 2023. Zheng J, Xin Y,  Zhang J, Subramanian R, Murray BP ,  Whitney JA ,  Warr MR , Ling J, Moorehead L, Kwan E,  Hemenway J, Smith BJ , and  Silverman JA  (2018) Pharmacokinetics and disposition of momelotinib revealed a disproportionate human metabolite-resolution for clinical development. Drug Metab Dispos 46: 237-247.
Data collection, definition of terms and analysis All demographic, circumstantial, clinical, and biological data were collected from the medical records of the ED. In a second step, we analyzed the cohort by comparing 2 subgroups: patients without and with complications. The occurrence of a complication was defined by: -the occurrence of death, -the need for resuscitation manoeuvres or hospitalization in the intensive care unit, -the use of an invasive procedure (surgery, digestive endoscopy), -the presence of clinical signs of toxin impregnation (defined by the presence of clinical signs that could not be explained by the arrest alone (stress, anxiety) AND by the presence of known clinical signs of cocaine poisoning AND by the presence of a positive urine test for cocaine (look for metabolite benzoylecgonine).
Objectives: To date, very few cannabis-based specialities are authorised on the French market despite a growing demand from patients and health professionals. The objective of this study is to review the tolerance profile and the French legislative status of the two main cannabinoids used for therapeutic purposes: tetrahydrocannabiol (THC) associated with psychoactive effects and non-psychoactive cannabidiol (CBD). Methods: This review is based on relevant articles retrieved by a search in Google Scholar and PubMed databases and on an assessment of the legal texts and summaries of product characteristics available in France. Results: Evidence for the tolerability of CBD during chronic use is reassuring, but a significant risk of drug interactions exists. THC use appears to be associated with a higher proportion of J o u r n a l P r e -p r o o f 2 serious adverse effects, including neuropsychological and cardiovascular effects.
To date, the question of adverse effects of cannabis has arisen in the context of its use for therapeutic purposes, particularly in view of the data available on its toxicity during recreational use  (3). The objective of this work is to conduct a literature review on medical cannabis toxicity data. Google Scholar and PubMed databases were used to search for relevant peer-reviewed articles published in English between 1970 and 2021 using the following search terms: cannabis, cannabinoids, phytocannabinoids, cannabidiol, medical, toxicity, chronic, long term, acute, toxic effects, safety, exposure, pharmacology, pharmacokinetics, pharmacodynamics, endocannabinoid system. Studies providing relevant information on the toxicity data of medical cannabis were included for analysis.
TOLERANCE DATA ON THE CHRONIC USE OF THERAPEUTIC CANNABIS The understanding of the efficacy and tolerance profile of therapeutic cannabis seems to have been limited by decades of prohibition in many countries. The acute toxicity of CBD and THC being well detailed in the literature  (38) (39) (40) , only the chronic toxicity of these 2 compounds is addressed here. In this context, any repeated exposure to cannabis or its compounds for at least 10 days was considered (sub)chronic and included for analysis. The main chronic toxicity data reported in humans are summarised in Table  2.
These elevations usually occur within two months of initiation of treatment, although cases have been observed up to 18 months after initiation of treatment, particularly in patients taking valproate  (48). In the Marinol® product monograph, it is also mentioned that THC can cause liver enzyme elevations, with clinical trial data reporting this adverse effect in ≤ 1% of cases  (49). Lastly, intestinal disorders including diarrhea, nausea and vomiting were reported as a frequent to very frequent adverse effect (from 1% to > 10% of patients) for the 3 cannabis specialities marketed in France (47-49).
It is said to be relatively easy to dissolve and inject  [9] [10] [11]. In France, the number of persons who inject oral slowrelease morphine regularly is estimated between 1075 and 1288 individuals, and 949 for occasional users  [6, 12]. Based on field and pharmacoepidemiological studies, the persons concerned by this practice are often precarious, sometimes nomadic in their access to care, and difficult to assess in conventional observational studies  [9, 10, 13]. The estimate of 2000 to 2300 individuals is obviously an underestimate since it was based on Health Insurance reimbursement data. These databases include only data on medications delivered to pharmacies and reimbursed by the Health Insurance. Deliveries paid directly by the user to the pharmacy, or purchases made on illegal markets are not recorded in these databases.
The characteristics of people who never receive reimbursement for their morphine are currently unknown. Consequently, their expectations regarding the most suitable harm reduction material for this practice, and possible experimentation associated with access to injectable substitution therapy remain unknown. The purpose of this online survey among people who inject oral forms of morphine intravenously was to collect data on field practices regarding the medications used and their procurement, dissolution and filtration techniques, injection equipment, and their expectations regarding a possible intravenous substitution treatment. Secondly, we assessed the potential characteristics of persons who obtain their morphine exclusively from illicit markets compared to those who receive their morphine from prescriptions dispensed in pharmacies.
Data analysis The responses were collected using REDCap ® software, ensuring electronic data acquisition and security  [17]. The REDCap web platform (https:// proje ct-redcap. org) possesses the authorization of the French personal data protection authority. In order to guarantee respondent anonymity, no IP addresses and/or tracking cookies were collected for this survey. In order to preserve the anonymity of the respondents as much as possible, it was necessary to manually check for duplicates and/or multiple respondents by comparing the dates and times of connection to the questionnaire and the proximity of the responses provided. In these cases, the questionnaires were excluded from the analyses. The data were described by numbers and associated percentages for categorical variables and by the mean ± standard deviation [minimum-maximum] for quantitative variables.
HES histopathology and Ki67 immunohistochemistry Histology was processed by ANEXPLO platform, Toulouse, France as previously described  (Bouclier et al. , 1991). After paraffin inclusion, 4 μm skin slices were stained with HES (Haematoxylin/Eosin/Safran). Keratinocytes and fibroblasts proliferation was evaluated by Ki67 immunohistochemistry (Anti-human and mouse Ki67 antibody clone sp6, Neomarkers RM-9106, California, USA). Stained slices were digitalized at microscope objective ×20 with scanner Pannoramic® 250, 3D Histech Ltd (Budapest, Hungary). Anatomo-pathological quantitative evaluation was performed by images analysis with QuPath software (Bankhead, P. et al. QuPath: Open-source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017) ) to permit the non-biased analysis of the following parameters: utriculi surface measurements, sebaceous glands surface, epidermis thickness, dermis inflammation (nuclei counting in back skin dermis, mean of 3 areas per slice), and keratinocytes and fibroblasts proliferation (counting of Ki67 positive cells per mm 2 in back skin epidermis and dermis respectively, mean of 6 areas per slice).
Data and statistical analysis All data are expressed as individual data or as means ± SEM of one experiment. For statistical analysis, differences were determined by ANOVA with significance set at the level of P < 0. 05. A Tukey's post hoc test for multiple comparisons was used to determine differences as indicated. Differences between groups were evaluated for each toxin in unpaired t tests. All data processing and statistical tests were done using GraphPad Prism version 8 (GraphPad Software Inc. , La Jolla, CA, USA).
The data are the observed mean (+SEM) of n = 6 mice. All groups were compared to the vehicle group, only significant difference was signaled, no * means NS (not significant), *p < 0. 05, **p < 0. 01, ***p < 0. 001. Fig. 5. The dorsal area of each mouse was sampled at day 15. The skin was excised and homogenized, and cytokines levels were determined by ELISA. Substance P (A) and pro-inflammatory IL-1α cytokine level (B) were measured in back skin extracts. The data are the observed mean (+SEM) of n = 6 mice. All groups are compared to the vehicle group, only significant difference is signaled, no * means NS (not significant), *p < 0. 05, **p < 0. 01, ***p < 0. 001. Fig. 6. Skin surface lipids (SSL) were collected by applying small glass rods on mouse back skin treated zone at day 15 before euthanasia.
Total sebum quantity was measured (A) and sebum lipid composition was performed by GC/FID analysis. Cholesterol (B), squalene (C), fatty acid FA1 (D), FA2 (E), FA3 (F), FA4 (G), waxes WX1 (H), WX2 (I), WX3 (J), WX4 (K) and cholesteryl esters (L) levels were evaluated. The data are observed means (+SEM) obtained from n = 6 mice. All groups are compared to the vehicle group, only significant difference is signaled, no * means NS (not significant), *p < 0. 05, **p < 0. 01, ***p < 0. 001. commonly observed with retinoid treatment, such as visually observed lesions (e. g. scaling and erythema), and anatomo-pathological lesions observed (e. g. epidermis hyperplasia and dermis inflammation). AboBoNT-A did not enhance cell proliferation in epidermis and dermis and decreased cells infiltration (nuclei numbering) in dermis from the dose of 0.
Hypothetically, hairless mice might appear with oil-like skin not due to overproduction of sebum, but due to absence of hair. In conclusion, while a specific therapeutic effect on acne was not recorded in this study, a beneficial effect on skin quality was obtained. Beyond acne, these data support the multiple properties of aboBoNT-A in improving skin properties through its positive effect on skin structures and inflammatory level, as well as its effect on the sebum concentration of several lipids with skin protective properties.
since it is mostly involved in these activities. Therefore, in this current study, we attempt to compare the phenolic profiles of NS and ND seed extracts by the determination of total phenolic, total flavonoid, and tannin contents, as well by identifying the individual phenolics by high performance liquid chromatography with diode array detection (HPLC-DAD). Furthermore, the in vivo and in vitro antioxidant and antimicrobial properties of these extracts were assessed. Additionally, to overcome the lack of data about the proximate composition of the seeds of NS and ND, some nutritional constituents, including carbohydrates, proteins, fats, and mineral levels were also evaluated. Beforehand, to facilitate the botanical discrimination of the studied species, macroscopic and microscopic parameters were determined for the different species parts.
Identification and Quantification of Phenolic Compounds Using HPLC-DAD Qualitative and quantitative analyses of polyphenols in NS and ND methanolic extracts were performed by HPLC-DAD. The chromatograms resulting from both species have almost the same profile (Figure  3 ), and similar phenolic derivatives were detected with a difference in the amounts of constituents. According to the results (Table  4 ), hydroxybenzoic and caffeic acids were the major phenolic compounds of both species. They were followed by vanillic, syringic, gallic, and p-coumaric acids, and catechin. Nevertheless, low amounts of cinnamic acid and quercetin were noticed. Still, ND seed extract contains higher amounts of individual phenolic compounds than NS extract. Data concerning the HPLC-DAD phenolic profiles of the studied species are rather scarce in the literature, mainly for ND extract, and revealed a discrepancy in the results of their qualitative and quantitative phenolic composition, especially the major compounds.
Acknowledgments: We thank all the members of laboratory  3BS  of the  University of Bejaia. We also thank the members of laboratory  UMR CNRS 6226 of the "Ecole Nationale Supérieure de Chimie de Rennes". We wish to thank  Elsebai Mahmoud Fahmi  from the  Department of Pharmacognosy, Faculty of Pharmacy, Mansoura University, Egypt  for English language correction. We also present our thanks to  Malika Ourari  from the  University of Bejaia  and  Gaëlle Saladin  from the  University of Limoges  for the botanical identification of the two Nigella species. We also wish to thank all persons who have participated in the realization of this research.
Introduction. -Nirmatrelvir/ritonavir (Paxlovid ® ) is currently one of the few therapeutic options for coronavirus disease 2019 (COVID-19) curative treatment in non-oxygen-requiring adult patients at-high risk of progressing to severe disease. This recently approved boosted antiviral therapy presents a significant risk of drug-drug interactions (DDI). As part of the enhanced surveillance program in France for COVID-19 drugs and vaccines, the French national pharmacovigilance database (BNPV [base nationale de pharmacovigilance]) was queried in order to better characterize the drug safety profile, with a special focus on DDI. The aim of the study was to describe the adverse drug reactions reported through the BNPV.
In addition to these issues inherent to the risk of DDI, the most expected adverse effects of nirmatrelvir/ritonavir are +Model THERAP-785; No. of  Pages 17  Therapie xxx (xxxx) xxx-xxx diarrhea, headache, vomiting, nausea and dysgeusia (metallic/bitter taste in the mouth after each drug intake)  [3]. All these adverse effects are common but generally resolve upon discontinuation. As part of the enhanced surveillance in France for COVID-19 drugs and vaccines, we thus queried the national (French) pharmacovigilance database (called BNPV) in order to better characterize the clinical, paraclinical, and prognostic features of the safety profile of nirmatrelvir/ritonavir, with particular attention to DDI.
Method Reports identification process All reports validated in BNPV from the first authorization (January 20th, 2022) to December 3rd, 2022 (date of the query) were considered. Briefly, the BNPV gathers spontaneous reports of adverse drug reactions from French healthcare professionals or patients. Each report is assessed by clinical pharmacologists in the relevant regional pharmacovigilance center -according to the French method for the causality assessment of adverse drug reactionsbefore being recorded in the database. The French method for the causality assessment of adverse drug reactions (also named ''imputability'') takes into account the following parameters  [5, 6] : • intrinsic ''imputability'', which ranges from I0 (no association between the reaction and a drug) to I6 (strong association between the events) and combines a chronological score (temporal link) and a semiological score (etiological link), each ranging from 0 to 3; • extrinsic score, based on previously published similar reports (bibliographic documentation), which ranges from B1 (adverse drug reaction not published) to B4 (expected adverse drug reaction listed in SmPC).
Discussion Analysis of reports reported during the investigation period (from January, 28th 2022 to December, 3rd 2022) confirms the safety profile of nirmatrelvir/ritonavir mentioned in the current SmPC, notably its complex drug-drug interaction profile linked to ritonavir. In addition, this analysis has also allowed identifying the unexpected adverse events that would need to be investigated more accurately to assess drug causality as follows: high blood pressure, confusion, acute renal failure and various cutaneous eruptions. More than 10% of reports retrieved in the French Pharmacovigilance database are DDI reports. Apart from 2 reports of hematoma discovered during heparin therapy (as a relay of an oral anticoagulant) and the INR variation during fluindione treatment, all currently reported DDI were known and therefore expected taking into account the known pharmacokinetic interaction profile of ritonavir.
The systematic search for potential DDI before any initiation of nirmatrelvir/ritonavir is essential. As mentioned in the SmPC, a multidisciplinary approach (involving physicians and specialists in clinical pharmacology) is recommended for the management of DDI in some patients receiving multiple concomitant medications. For that purpose, in France, the General Directorate for Health in collaboration with the French Society of Pharmacology & Therapeutics, the national network of regional pharmacovigilance centers and the network of pharmacology laboratories, has put in place a device through a toll-free phone number to support the prescribing of Paxlovid ® and to help prescribers and dispensers for the management of DDI  [31]. Regarding elevated blood pressure (which is a listed adverse effects in the Norvir ® SmPC)  [30] , the seven reported reports in the French Pharmacovigilance database were mostly not serious and transient with a spontaneous regression after nirmatrelvir/ritonavir discontinuation, without symptomatic treatment.
Data used for ML and PK/PBPK model development To ensure data consistency and to mitigate variability due to assay choice, historical data generated exclusively in the Sanofi Boston site were used. These data were derived from two datasets: the compound property dataset, where structural, PC, and PK parameters are stored, and the PK profile dataset, where concentration time profiles for the different compounds are saved. The compound property dataset consisted of 530 compounds for which in vivo rat CL and Vdss were calculated based on NCA of 1 mg/kg IV administration in rats, and 451 compounds for which CL int was calculated based on in vitro microsomal experiments. There were 459 and 324 compounds with available information for pka (most acidic) and pka (most basic), respectively, and only 188 compounds for which Fu data were available.
SMILES (simplified molecular-input line-entry system) string format was available for each compound. The PK profile dataset consisted of 397 compounds for which we had rat PK profile (concentration vs. time data) for 1 mg/kg IV administration in rats. A total of 637 unique compounds were obtained after combining the aforementioned datasets, of which 61 compounds were found to be common across all. As shown by chemical similarity distribution based on Tanimoto score (Supplementary Figure  S1 ), most compounds in our dataset differed from each other.
Machine learning methodology As a first step in our proposed framework, ML models were built to predict the parameters required as input to mechanistic models. Details about the data used to develop these models are provided in Table  2. In-house ML models were built for fraction unbound (Fu), in vivo CL, in vivo Vdss, CL int , pKa (mostly acidic), and pKa (mostly basic). For molecular weight and lipophilicity (logP), they Python RDkit package was utilized to generate predictions  (Swain, 2016b; Cheminformatics, 2022; RDKit, 2022; RDKit, 2023). SMILES strings-one of the most widely used ways to represent a molecule-were used as input to the ML model. For most structures, one part of the SMILES string consists of salt, and another part represents the base chemical structure for the compound; thus, initial data cleaning was performed prior to training the ML models.
In the data cleaning process, salts were stripped from the molecules. In the pre-processing step, the SMILES strings were converted into their canonical forms, and SMILES strings were standardized using the MolVS package  (Swain, 2016a)  in Python. The SMILES strings, prior to feeding into the algorithm, were transformed into molecular descriptors (200 structure-based descriptors (RDKit, 2022)), molecular fingerprints (Morgan fingerprints, 1024 bit), and graphs (undirected graph represented as a 3-tuple (atom_features, bond_features, and pair_indices))  During cross-validation, hyperparameter optimization was also performed : for random forest algorithm, they were the number of estimators and max depth; for XGBoost, they were the number of estimators, subsample, colsample by tree, and learning rate; for support vector regression, they were kernel, gamma, regularization parameter, and epsilon  (Pedregosa et al.
, 2011) ; for message passing neural network, they were the number of layers, number of neurons, and activation function  (Kensert, 2021). Different molecular representations (molecular fingerprints, descriptors, and graphs (specifically for message-passing neural networks (MPNN))) were initially tested to determine which molecular representation had the best performance metrics (RMSE, MAPE) for our datasets. Different ML algorithms (random forest  (Liaw and Wiener, 2002) , support vector regression (SVR)  (Awad and Khanna, 2015) , XGboost  (Chen and Guestrin, 2016) , and MPNN  (Gilmer et al. , 2020) ) were also tested to see which had the best performance metrics (Supplementary Table  S1 ). To avoid biased predictions (predictions of PK profile generated for compound available in training set) and have uniform predictions generated from one compartment model and PBPK model, 61 compounds for which we had all associated data (PK, PC parameters, and PK profile) were used in the test set for all the models.
PK/PBPK model characteristics and assumptions 1-Compartment modeling The 1-compartment model assumes that the distribution of the drug takes place homogeneously in a single volume of distribution (Vdss), and its clearance is mediated by a unique rate of elimination (CL) following first-order kinetics  (Talevi and Bellera, 2021). The one-compartment model was developed using MATLAB R2019a.
Results Figures 2A-F show the measured vs. predicted values for each of the parameters tested. ML predictions for CL, Vdss, CLint, pKa (most acidic), and pKa (most basic) were found to be reasonable, with less than two-fold error (MAPE <1) for the final model. MAPE corresponding to fraction unbound was 1. 11, which may be due to the low amount of data in the training set (Figure  2F ). It was found for this dataset that deep learning methods were less accurate than the classical approach, perhaps due to a low amount of data used in the training set. Finally, it was observed that the MPNN model (deep learning) converged to the mean value of the parameter and hence was not able to capture the distribution spread. Performance metrics of the final model used for ML predictions are provided in Table  2 , and performance metrics of different models tested for this analysis are provided in Supplementary Table  S1.