Caio Henrique Pinke Rodrigues

Bio: Caio Henrique Pinke Rodrigues is an academic researcher from Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto. The author has contributed to research in topics: Identification (biology) & Chemometrics. The author has an hindex of 2, co-authored 12 publications receiving 16 citations. Previous affiliations of Caio Henrique Pinke Rodrigues include University of São Paulo.

Vidros automobilísticos como vestígios de cena de crime: uma abordagem multivariada

Abstract: AUTOMOBILISTIC GLASSES AS CRIME SCENE TRACES: A MULTIVARIATE APPROACH. Glasses are common trace evidence elements in crime scenes, and the analysis of this material can be essential for evaluating different criminal dynamics. This work aimed to analyze the possibility of differentiating and classifying windshield glass using multivariate analysis methods. Automotive glass fragments from different vehicle brands were evaluated according to internal and external faces. We have collected from literature EDXRF (Energy Dispersive X-ray Fluorescence) data for different oxides concentrations. These data were organized in a matrix with 56 samples and nine variables. We applied unsupervised (PCA, Principal Component Analysis) and supervised (SIMCA, Soft Interclass Modeling Classification Analogy) methods. We assessed the classification responses through ROC (Receiver Operating Characteristics). As a result, the PCA indicated the presence of two groups of glasses in three main components. SIMCA verified the unsupervised classification, and the distances and interclass residues parameters were adequate with no outliers. The ROC analysis indicated a sensitivity of 0.793, a specificity of 0.815, and an efficiency of 0.804 for predictions. We concluded that multivariate analysis was successful in discriminating between the internal and external faces of automotive glasses.

In Silico Infrared Spectroscopy as a Benchmark for Identifying Seized Samples Suspected of Being N-Ethylpentylone

Abstract: New psychoactive substances (NPSs) have concerned authorities worldwide, and monitoring them has become increasingly complex. In addition to the frequent emergence of new chemical structures, the composition of adulterants has changed rapidly. Reliable reference data on NPS are not always available, and identifying them has become an operational problem. In this study, we evaluated the infrared spectral data of 68 seized samples suspected of containing a synthetic cathinone (N-ethylpentylone). We used quantum chemistry tools to simulate infrared spectra as a benchmark and obtained infrared spectra for different cathinones, structurally analogous amphetamines, and possible adulterants. We employed these in silico data to construct different chemometric models and investigated the internal and external validation and classification requirements of the models. We applied the best models to predict the classification of the experimental data, which showed that the seized samples did not have a well-defined profile. Infrared spectra alone did not allow N-ethylpentylone to be distinguished from other substances. This study enabled us to evaluate whether experimental, in silico, and applied statistical techniques help to promote forensic analysis for decision-making. The seized samples required in-depth treatment and evaluation so that they could be correctly analyzed for forensic purposes.

In silico Evaluation of Cucurbit[6]uril as a Potential Detector for Cocaine and Its Adulterants Lidocaine, Caffeine, and Procaine

Abstract: Illicit drugs and their trafficking require worldwide efforts in investigation, detection, and control. Colorimetric tests are often applied to identify drugs. Cocaine has some well-known adulterants that can provide a false positive response. Cucurbit[6]uril (CB[6]) has been suggested as a potential detector for cocaine and other illicit drugs. This work uses in silico methods to evaluate the use of CB[6] to detect cocaine and these interfering substances. More specifically, this work analyzes different possibilities of CB[6] complexation with cocaine, lidocaine, caffeine, and procaine and compares the results achieved for cocaine and its adulterants. Different methodologies were employed: quantum chemistry was investigated through DFT B3LYP/TZVP (density functional theory-Becke, three-parameter, Lee-Yang-Parr with triple zeta valence plus polarization basis set) and the semi-empirical methods Austin model 1 (AM1), parametric methods 3, 6, and 7 (PM3, PM6, PM7), and Recife model 1 (RM1). We used these methodologies intending to compare the reasonability and reproducibility of the results in the gas phase condition. Solvent influence was studied by molecular dynamics (MD) simulations. Results showed that CB[6] does not bind to these substances, as judged from the positive values of binding free energy obtained with all methods. DFT and MD were the most reliable methods whereas semiempirical ones were not reproductible in describing these systems. Results also showed that interactions are not specific, so CB[6] does not provide a good response for cocaine detection.

Analytical Challenges for Identification of New Psychoactive Substances: A Literature-Based Study for Seized Drugs

Abstract: Correct identification of substances is essential to understand drug use and trafficking trends and guide measures for harm reduction and treatment. Two steps are needed to verify the nature of a substance properly: a presumptive test and a confirmatory test. There are presumptive tests which presents deficiencies, such as providing false-positive and false-negative results. Confirmatory tests are more reliable, but they are more expensive. With the appearance of New Psychoactive Substances (NPS), identifying and characterizing illicit substances has become more challenging. This paper focuses on presenting information about NPS characteristics and analysis. For this purpose, we have reviewed the literature to address the main aspects of five groups of NPS: amphetamine-type stimulants, synthetic cannabinoids, N-methoxybenzyl-methoxyphenylethylamine (NBOMe), synthetic opioids, and benzodiazepines. We present the main characteristics of each group and certain aspects of presumptive and confirmatory tests regarding these groups. Our findings show obstacles in developing methodologies that can correctly identify these substances, and problems can increase as new structures appear. This information can be helpful to drive research into identifying NPS and inform law enforcement and law practitioners about the main characteristics of each group and the main questions involving their identification.

In silico infrared characterization of synthetic cannabinoids by quantum chemistry and chemometrics.

Abstract: The concept of forensic sciences as mere trace analysis has been modified by the idea of forensic intelligence, which entails applying data to make decisions within the investigative process. Many countries are engaged in combating drug trafficking and drug use because they are related to public health and safety issues. Prohibiting the consumption of traditional drugs has led new psychoactive substances (NPSs) to emerge. NPSs consist of compounds that resemble the initially banned substance and which aim to mimic the traditional drug recreational effects while circumventing drug legislation. For example, synthetic cannabinoids are sprayed on herbal products to reproduce the cannabis recreational effects. According to the United Nations Office on Drugs and Crime (UNODC), the toxic effects of synthetic cannabis types are unknown, and harm and fatalities associated with the use of these drugs have been reported. Information on the characterization related to these species is lacking. The rate at which NPSs appear poses a significant challenge because employing conventional methods to understand the characteristics of these compounds may require time and be costly. This work uses in silico practices as an alternative to understand how NPSs related to cannabis behave. We apply quantum chemistry methods to evaluate several synthetic cannabinoids recognized in forensic samples. More specifically, we generate infrared spectra that can be employed as a benchmark for NPSs. We apply a multivariate classification to evaluate the results. We conclude that in silico methods are an alternative that provide information about the spectra of undetected substances. This information can help to identify new drugs, to increase knowledge about them, and to feed information procedures.