Class identity assignment for amphetamines using neural networks and GC-FTIR data.

An exploratory analysis was performed in order to evaluate the feasibility of building of neural network (NN) systems automating the identification of amphetamines necessary in the investigation of drugs of abuse for epidemiological, clinical and forensic purposes. A first neural network system was built to distinguish between amphetamines and nonamphetamines. A second, more refined system, aimed to the recognition of amphetamines according to their toxicological activity (stimulant amphetamines, hallucinogenic amphetamines, nonamphetamines). Both systems proved that discrimination between amphetamines and nonamphetamines, as well as between stimulants, hallucinogens and nonamphetamines is possible (83.44% and 85.71% correct classification rate, respectively). The spectroscopic interpretation of the 40 most important input variables (GC-FTIR absorption intensities) shows that the modeling power of an input variable seems to be correlated with the stability and not with the intensity of the spectral interaction. Thus, discarding variables only because they correspond to spectral windows with weak absorptions does not seem be not advisable.

Structure-activity correlations for illicit amphetamines using ANN and constitutional descriptors.

The goal of this study was to develop an expert system capable to identify the potential biological activity of new substances having a molecular structure similar to illicit amphetamines. For this purpose we have designed two types of artificial neural network (ANN) systems, which have been trained to classify amphetamines according to their toxicological activity (stimulant amphetamines or hallucinogenic amphetamines) and distinguish them from nonamphetamines. Such a system is essential for testing new molecular structures for epidemiological, clinical, and forensic purposes. The first type of artificial neural network is a "spectral" neural network, which has as input variables the most important 100 absorption intensities from a total of 260 measured for each normalized infrared spectrum 10cm(-1) apart. The spectral data consists of a database built with the GC-FT-IR spectra of the most popular drugs of abuse (mainly central stimulants, hallucinogens, sympathomimetic amines, narcotics and other potent analgesics), precursors and derivatized counterparts. All samples were also characterized by their constitutional descriptors (CDs). For each sample, a number of 45 CDs were computed and introduced as input variables for a second type of ANN, which uses a structural database. The efficiency of this "structural" artificial neural network (CD-ANN) has been improved by optimizing the training set and increasing the number of input variables (CDs). A comparative analysis of the spectral and the structural networks is presented.

Chemometric detection of thermally degraded samples in the analysis of drugs of abuse with gas chromatography-Fourier-transform infrared spectroscopy.

We present a chemometric procedure for the identification of the reference standard chromatographic peak in cases where the GC-FTIR analysis of commercial standards results in the appearance of more than one peak in the GC chromatogram. The procedure has been designed for phenethylamines, which represent the class with the largest number of individual molecules on the illicit drug market, and which are abused for their stimulant and/or hallucinogenic effects. The similarity between their vapor-phase FTIR spectra was modeled using principal component analysis (PCA), and class identity was assigned on the basis of soft independent modeling of class analogy (SIMCA). Additional peaks could be assigned to impurities in the standards, but most often they were artifacts formed during the GC-FTIR analysis of thermolabile or chemically unstable compounds. The latter case is illustrated by the identification of the reference standard chromatographic peak and FTIR spectrum of the potent psychotropic amphetamine derivative N-methyl-1-(3,4-methylenedioxyphenyl)-2-butanamine (MBDB), and by the elucidation of the chemical changes that occur in the molecule of MBDB due to thermal degradation.

Computer-aided screening for hallucinogenic and stimulant amphetamines with gas chromatography-Fourier transform infrared spectroscopy (GC-FTIR).

An expert system applied as a screening test for amphetamine analogues found in recreational-drug exhibits (tablets or powders) is described. The knowledge base defining the reference Fourier transform infrared spectroscopic (FTIR) spectral patterns has been built according to criteria encompassing toxicological, pharmacological, and neurochemical aspects. The class identity of a compound is determined within seconds using soft independent modeling of class analogy (SIMCA). The predictive value of the system, as assessed at a testing accuracy of 95%, is expressed by a total correct classification rate of 93.93% and by a 96.30% rate of true-positive amphetamines. The specificity and the selectivity of the screening test, evaluated by testing 159 toxicologically relevant compounds, are discussed, emphasizing the chemical and physical factors affecting these parameters. Medicinal amphetamines giving cross-reactions with traditional screening techniques produce a negative result. The specificity of the system characterizes the expert system as a highly sensitive, selective, fast, and user-friendly screening test that screens for amphetamines with prediction accuracy adequate for investigations in analytical toxicology.

Identification of novel illicit amphetamines from vapor-phase FTIR spectra - a chemometrical solution.

A computer-aided procedure automating the identification of illicit amphetamine analogs eluting from a gas chromatograph coupled to a Fourier transform infrared spectrometer is presented. The expert system discriminates novel amphetamines from other classes of drugs of abuse normally screened in illicit tablets or powders. The main analytical advantages of the system over the automated procedures dedicated to general unknown analysis are the objectivity and the accuracy in predicting the class identity of the compound (i.e. stimulant, hallucinogen) when the reference spectrum is not present in the spectral library. The expert system uses quantitative thresholds defining the similarity of the unknown to the classes of illicit amphetamines and checks the presence of the molecular skeletons associated with different psychotropic effects of amphetamines. The challenge in building the system was the fuzziness of vapor-phase Fourier transform infrared spectrometer spectra of low-weight molecules such as amphetamines. This paper emphasizes the chemometrical techniques found most appropriate for modeling such spectral behavior. An exploratory (principal component) analysis indicated the sample preparation and the feature weight function yielding the best input for the knowledge base. The class identity of a compound was assigned using Soft Independent Modeling of Class Analogy. A rule-based decision system was implemented to enhance the accuracy in identity assignment. The flow diagram optimizing the knowledge base content of each model is presented. Finally, up to 81.13% (out of 159 tested compounds) were classified with a 5% confidence level. The total correct classification rate was 93.93%, for a yield of 96.30% true positive amphetamines.

Pattern recognition techniques screening for drugs of abuse with gas chromatography-Fourier transform infrared spectroscopy.

As many drugs of abuse are relatively volatile substances, gas chromatography-mass spectrometry (GC-MS), and more recently gas chromatography-Fourier transform infrared spectroscopy (GC-FTIR) became the most powerful techniques applied for their identification. We are presenting a combination of pattern recognition techniques discriminating illicit amphetamines according to the substitution pattern associated with the psychotropic activity (stimulants and hallucinogens) for which they are abused, and with the corresponding level of health hazard. As we determined, GC-FTIR provides the best selectivity in identifying the structural features associated with the full constellation of pharmacological effects of amphetamines. The toxicological questions to be answered and the spectroscopic features enabling the screening based on soft independent modeling of class analogy (SIMCA) are discussed. The accuracy, sensitivity and selectivity of the system recommend its use for automating the investigations of illicit drugs for epidemiological, clinical, administrative and forensic purposes. As opposed to the traditional tests screening for drugs of abuse, the system may also be applied as a broad-spectrum screening test. The extent to which the output of a query for amphetamines may be used for assessing the class identity of a negative (i.e. other hallucinogens or stimulants, sympathomimetic amines, narcotics and precursors) was determined by a systematic principal component analysis (PCA). The basic information is summarized in tables according to the category or class of compounds found suitable for screening.