Using cheminformatics to predict cross reactivity of "designer drugs" to their currently available immunoassays

False-Positive Amphetamines in Urine Drug Screens: A 6-Year Review

Immunoassays are routinely used to provide rapid urine drug screening results in the clinical setting. These screening tests are prone to false-positive results and ideally require confirmation by mass spectrometry. In this study, we have examined a large number of urine specimens where drugs other than amphetamines may have caused a false-positive amphetamine immunoassay screening result. Urine drug screens (12,250) in a clinical laboratory that used the CEDIA amphetamine/ecstasy method were reviewed for false-positive results over a 6-year period (2015-2020). An additional 3,486 referred samples, for which confirmatory--mass spectrometry was requested, were also reviewed. About 86 in-house samples and 175 referral samples that were CEDIA false-positive screens were further analyzed by an LC-QTOF general unknown screen. Potential cross-reacting drugs were identified, and their molecular similarities to the CEDIA targets were determined. Commercial standards were also analyzed for cross-reactivity in the amphetamine/ecstasy CEDIA screen. Positive amphetamine results in 3.9% of in-house samples and 9.9% of referred tests for confirmatory analysis were false positive for amphetamines. Of these false-positive specimens, on average, 6.8 drugs were detected by the LC-QTOF screen. Several drugs were identified as possible cross-reacting drugs to the CEDIA amphetamine/ecstasy assay. Maximum common substructure scores for 70 potential cross-reacting compounds were calculated. This was not helpful in identifying cross-reacting drugs. False-positive amphetamine screens make up to 3.9-9.9% of positive amphetamine screens in the clinical laboratory. Knowledge of cross-reacting drugs may be helpful when mass spectrometry testing is unavailable.

Amphetamine-type stimulants (ATS) drug classification using shallow one-dimensional convolutional neural network

Amphetamine-type stimulants (ATS) drug analysis and identification are challenging and critical nowadays with the emergence production of new synthetic ATS drugs with sophisticated design compounds. In the present study, we proposed a one-dimensional convolutional neural network (1DCNN) model to perform ATS drug classification as an alternative method. We investigate as well as explore the classification behavior of 1DCNN with the utilization of the existing novel 3D molecular descriptors as ATS drugs representation to become the model input. The proposed 1DCNN model is composed of one convolutional layer to reduce the model complexity. Besides, pooling operation that is a standard part of traditional CNN is not applied in this architecture to have more features in the classification phase. The dropout regularization technique is employed to improve model generalization. Experiments were conducted to find the optimal values for three dominant hyper-parameters of the 1DCNN model which are the filter size, transfer function, and batch size. Our findings found that kernel size 11, exponential linear unit (ELU) transfer function and batch size 32 are optimal for the 1DCNN model. A comparison with several machine learning classifiers has shown that our proposed 1DCNN has achieved comparable performance with the Random Forest classifier and competitive performance with the others.

Overview of Synthetic Cannabinoids ADB-FUBINACA and AMB-FUBINACA: Clinical, Analytical, and Forensic Implications

ADB-FUBINACA and AMB-FUBINACA are two synthetic indazole-derived cannabinoid receptor agonists, up to 140- and 85-fold more potent, respectively, than trans-∆⁹-tetrahydrocannabinol (∆⁹-THC), the main psychoactive compound of cannabis. Synthesised in 2009 as a pharmaceutical drug candidate, the recreational use of ADB-FUBINACA was first reported in 2013 in Japan, with fatal cases being described in 2015. ADB-FUBINACA is one of the most apprehended and consumed synthetic cannabinoid (SC), following AMB-FUBINACA, which emerged in 2014 as a drug of abuse and has since been responsible for several intoxication and death outbreaks. Here, we critically review the physicochemical properties, detection methods, prevalence, biological effects, pharmacodynamics and pharmacokinetics of both drugs. When smoked, these SCs produce almost immediate effects (about 10 to 15 s after use) that last up to 60 min. They are rapidly and extensively metabolised, being the O-demethylated metabolite of AMB-FUBINACA, 2-(1-(4-fluorobenzyl)-1H-indazole-3-carboxamide)-3-methylbutanoic acid, the main excreted in urine, while for ADB-FUBINACA the main biomarkers are the hydroxdimethylpropyl ADB-FUBINACA, hydroxydehydrodimethylpropyl ADB-FUBINACA and hydroxylindazole ADB-FUBINACA. ADB-FUBINACA and AMB-FUBINACA display full agonism of the CB1 receptor, this being responsible for their cardiovascular and neurological effects (e.g., altered perception, agitation, anxiety, paranoia, hallucinations, loss of consciousness and memory, chest pain, hypertension, tachycardia, seizures). This review highlights the urgent requirement for additional studies on the toxicokinetic properties of AMB-FUBINACA and ADB-FUBINACA, as this is imperative to improve the methods for detecting and quantifying these drugs and to determine the best exposure markers in the various biological matrices. Furthermore, it stresses the need for clinicians and pathologists involved in the management of these intoxications to describe their findings in the scientific literature, thus assisting in the risk assessment and treatment of the harmful effects of these drugs in future medical and forensic investigations.

Synthetic drugs of abuse

Synthetic drugs of abuse contain various psychoactive substances. These substances have recently emerged as novel drugs of abuse in public; thus, they are known as novel psychoactive substances (NPS). As these compounds are artificially synthesized in a laboratory, they are also called designer drugs. Synthetic cannabinoids and synthetic cathinones are the two primary classes of NPS or designer drugs. Synthetic cannabinoids, also known as "K2" or "Spice," are potent agonists of the cannabinoid receptors. Synthetic cathinones, known as "Bath salts," are beta-keto amphetamine derivatives. These compounds can cause severe intoxication, including overdose deaths. NPS are accessible locally and online. NPS are scheduled in the US and other countries, but the underground chemists keep modifying the chemical structure of these compounds to avoid legal regulation; thus, these compounds have been evolving rapidly. These drugs are not detectable by traditional drug screening, and thus, these substances are mainly abused by young individuals and others who wish to avoid drug detection. These compounds are analyzed primarily by mass spectrometry.

Trends using biological target-based assays for drug detection in complex sample matrices

In vivo, drug molecules interact with their biological targets (e.g., enzymes, receptors, ion channels, transporters), thereby eliciting therapeutic effects. Assays that measure the interaction between drugs and bio-targets may be used as drug biosensors, which are capable of broadly detecting entire drug classes without prior knowledge of their chemical structure. This Trends article covers recent developments in bio-target-based screening assays for detecting drugs associated with the following areas: illicit products marketed as dietary supplements, food-producing animals, and bodily fluids. General challenges and considerations associated with using bio-target assays are also presented. Finally, future applications of these assays for drug detection are suggested based upon current needs.

Novel ketamine analogues cause a false positive phencyclidine immunoassay

Background

Immunoassays are commonly used to test for drugs of abuse in patients in a variety of settings. The increasing prevalence of ‘designer’ drugs causes difficulties for the toxicology laboratory and may result in unexpected false positives and identification of unfamiliar compounds. Within the past decade, there have been a variety of ketamine and phencyclidine analogues identified, particularly as drugs of abuse.

Method

We present a case of intoxication with a novel ketamine analogue, deschloro-N-ethyl-ketamine, causing a false positive phencyclidine immunoassay. Additionally, we performed spiking studies and 2D molecular similarity calculations for deschloro-N-ethyl-ketamine, ketamine and three other analogues on the Siemens Viva-E EMIT-II phencyclidine assay to assess their cross-reactivity.

Results

Four of the tested compounds (deschloro-N-ethyl-ketamine, 3-methoxy-phencyclidine, 3-methoxy-eticyclidine and methoxetamine) cause false positive phencyclidine immunoassay results, while ketamine gives a negative result. The cross-reactivity data are in accord with the similarity calculations of these molecules, further validating the ability of 2D molecular similarity analysis to predict the molecular cross-reactivity in immunoassays.

Conclusions

The cross-reactivity data of phencyclidine and ketamine analogues presented in this study could help toxicology laboratories and clinicians in evaluating unexpected results, particularly when novel PCP and ketamine analogues are being considered.

Accidental intoxications in toddlers: lack of cross-reactivity of vilazodone and its urinary metabolite M17 with drug of abuse screening immunoassays

Background

Vilazodone is an FDA approved medication used to treat major depressive disorder. The authors describe two cases of accidental vilazodone exposure in toddlers who presented with symptoms similar to amphetamine exposure and also with unexplained positive amphetamine urine immunoassay drug screens. Given a lack of published data on cross-reactivity of vilazodone and its metabolites with drug of abuse screening tests, the authors investigated drug of abuse immunoassay cross-reactivity of vilazodone and metabolites using computational and empirical approaches.

Methods

To ascertain the likelihood that vilazodone would cross-react with drug of abuse screening immunoassays, the authors assessed the two-dimensional (2D) similarity of the vilazodone parent molecule and known metabolites to an array of antigenic targets for urine immunoassay drug screens. To facilitate studies of the commercially unavailable M17 metabolite, it was prepared synthetically through a novel scheme. Urine and serum were spiked with vilazodone and M17 into urine (200–100,000 ng/mL) and serum (20–2000 ng/mL) samples and tested for cross-reactivity.

Results

Computational analysis using 2D similarity showed that vilazodone and metabolites have generally low similarity to antigenic targets of common drug of abuse screening immunoassays, predicting weak or no cross-reactivity. The M17 metabolite had 2D similarity to amphetamines and tricyclic antidepressants in a range similar to some other compounds exhibiting weak cross-reactivity on these immunoassays. Cross-reactivity testing was therefore performed on two different urine amphetamines immunoassays and a serum tricyclic antidepressant immunoassay. However, actual testing of cross reactivity for vilazodone and the M17 metabolite did not detect cross-reactivity for any urine amphetamines screen at concentrations up to 100,000 ng/mL and for a serum tricyclic antidepressants assays at concentrations up to 2000 ng/mL.

Conclusion

While the vilazodone metabolite M17 has weak 2D structural similarity to amphetamines and tricyclic antidepressants, the current study did not demonstrate any experimental cross-reactivity with two different urine amphetamines immunoassays and a serum tricyclic antidepressant immunoassay. Vilazodone ingestions in young children present a diagnostic challenge in their similarity to amphetamine ingestions and the lack of routine laboratory tests for vilazodone. Further work is needed to understand the metabolic profile for vilazodone in children versus adults.

Electronic supplementary material

The online version of this article (10.1186/s12907-019-0084-9) contains supplementary material, which is available to authorized users.

Newly Emerging Drugs of Abuse

Drug use and the associated overdose deaths have been a serious public health threat in the United States and the world. While traditional drugs of abuse such as cocaine remain popular, recreational use of newer synthetic drugs has continued to increase, but the prevalence of use is likely underestimated. In this review, epidemiology, chemistry, pharmacophysiology, clinical effects, laboratory detection, and clinical treatment are discussed for newly emerging drugs of abuse in the following classes: (1) opioids (e.g., fentanyl, fentanyl analogues, and mitragynine), (2) cannabinoids [THC and its analogues, alkylindole (e.g., JWH-018, JWH-073), cyclohexylphenol (e.g., CP-47,497), and indazole carboxamide (e.g., FUB-AMB, ADB-FUBINACA)], (3) stimulants and hallucinogens [β-keto amphetamines (e.g., methcathinone, methylone), pyrrolidinophenones (e.g., α-PVP, MDPV), and dimethoxyphenethylamine ("2C" and "NBOMe")], (4) dissociative agents (e.g., 3-MeO-PCP, methoxetamine, 2-oxo-PCE), and (5) sedative-hypnotics (e.g., gabapentin, baclofen, clonazolam, etizolam). It is critically important to coordinate hospital, medical examiner, and law enforcement personnel with laboratory services to respond to these emerging threats.

A Difficult Challenge for the Clinical Laboratory: Accessing and Interpreting Manufacturer Cross-Reactivity Data for Immunoassays Used in Urine Drug Testing

Urine drug testing by immunoassay is widely used to detect nonmedical drug use and to monitor patients prescribed controlled substances. A key attribute of urine drug testing immunoassays is cross-reactivity, namely the response of various compounds compared to the target of the assay. In this report, we analyzed the variability in how manufacturer cross-reactivity data are summarized in package inserts for commercially available amphetamines, benzodiazepines, and opiates immunoassays, 3 broad drug classes commonly included in routine drug testing panels. Specifically, we determined the number of compounds tested for cross-reactivity, manner in which cross-reactivity is measured, concentration units used, how often compounds known to be cross-reactive with marketed urine drug testing immunoassays prior to 2010 were tested, availability of the package insert online, and how often cross-reactivity on "designer drugs" was found in the package inserts. There was wide variability in the number of compounds tested (both positive and negative), with the highest number of tested compounds generally found in point-of-care urine drug testing applications. Most package inserts used ng/mL as the concentration units and expressed cross-reactivity in terms of equivalent concentrations to the assay calibrator. Approximately 50% of package inserts were directly available online. Cross-reactivity data were sparse with respect to "off-target" drugs known to be cross-reactive prior to 2010 (an example being quinolone antibiotics and opiates immunoassays) and designer drugs. The present study indicates lack of consistency in cross-reactivity information in package inserts, complicating the interpretation of urine drug testing results. We use 3 example clinical cases to illustrate practical challenges accessing and interpreting cross-reactivity data.

Prediction of the Formation of Reactive Metabolites by A Novel Classifier Approach Based on Enrichment Factor Optimization (EFO) as Implemented in the VEGA Program

The study is aimed at developing linear classifiers to predict the capacity of a given substrate to yield reactive metabolites. While most of the hitherto reported predictive models are based on the occurrence of known structural alerts (e.g., the presence of toxophoric groups), the present study is focused on the generation of predictive models involving linear combinations of physicochemical and stereo-electronic descriptors. The development of these models is carried out by using a novel classification approach based on enrichment factor optimization (EFO) as implemented in the VEGA suite of programs. The study took advantage of metabolic data as collected by manually curated analysis of the primary literature and published in the years 2004⁻2009. The learning set included 977 substrates among which 138 compounds yielded reactive first-generation metabolites, plus 212 substrates generating reactive metabolites in all generations (i.e., metabolic steps). The results emphasized the possibility of developing satisfactory predictive models especially when focusing on the first-generation reactive metabolites. The extensive comparison of the classifier approach presented here using a set of well-known algorithms implemented in Weka 3.8 revealed that the proposed EFO method compares with the best available approaches and offers two relevant benefits since it involves a limited number of descriptors and provides a score-based probability thus allowing a critical evaluation of the obtained results. The last analyses on non-cheminformatics UCI datasets emphasize the general applicability of the EFO approach, which conveniently performs using both balanced and unbalanced datasets.

Ensemble learning method for the prediction of new bioactive molecules

Pharmacologically active molecules can provide remedies for a range of different illnesses and infections. Therefore, the search for such bioactive molecules has been an enduring mission. As such, there is a need to employ a more suitable, reliable, and robust classification method for enhancing the prediction of the existence of new bioactive molecules. In this paper, we adopt a recently developed combination of different boosting methods (Adaboost) for the prediction of new bioactive molecules. We conducted the research experiments utilizing the widely used MDL Drug Data Report (MDDR) database. The proposed boosting method generated better results than other machine learning methods. This finding suggests that the method is suitable for inclusion among the in silico tools for use in cheminformatics, computational chemistry and molecular biology.

Use of a data warehouse at an academic medical center for clinical pathology quality improvement, education, and research

BACKGROUND

Pathology data contained within the electronic health record (EHR), and laboratory information system (LIS) of hospitals represents a potentially powerful resource to improve clinical care. However, existing reporting tools within commercial EHR and LIS software may not be able to efficiently and rapidly mine data for quality improvement and research applications.

MATERIALS AND METHODS

We present experience using a data warehouse produced collaboratively between an academic medical center and a private company. The data warehouse contains data from the EHR, LIS, admission/discharge/transfer system, and billing records and can be accessed using a self-service data access tool known as Starmaker. The Starmaker software allows users to use complex Boolean logic, include and exclude rules, unit conversion and reference scaling, and value aggregation using a straightforward visual interface. More complex queries can be achieved by users with experience with Structured Query Language. Queries can use biomedical ontologies such as Logical Observation Identifiers Names and Codes and Systematized Nomenclature of Medicine.

RESULT

We present examples of successful searches using Starmaker, falling mostly in the realm of microbiology and clinical chemistry/toxicology. The searches were ones that were either very difficult or basically infeasible using reporting tools within the EHR and LIS used in the medical center. One of the main strengths of Starmaker searches is rapid results, with typical searches covering 5 years taking only 1-2 min. A "Run Count" feature quickly outputs the number of cases meeting criteria, allowing for refinement of searches before downloading patient-identifiable data. The Starmaker tool is available to pathology residents and fellows, with some using this tool for quality improvement and scholarly projects.

CONCLUSION

A data warehouse has significant potential for improving utilization of clinical pathology testing. Software that can access data warehouse using a straightforward visual interface can be incorporated into pathology training programs.

An Ab Initio Method for Designing Multi-Target Specific Pharmacophores using Complementary Interaction Field of Aspartic Proteases

For past few decades, key objectives of rational drug discovery have been the designing of specific and selective ligands for target proteins. Infectious diseases like malaria are continuously becoming resistant to traditional medicines, which inculcates need for new approaches to design inhibitors for antimalarial targets. A novel method for ab initio designing of multi target specific pharmacophores using the interaction field maps of active sites of multiple proteins has been developed to design 'specificity' pharmacophores for aspartic proteases. The molecular interaction field grid maps of active sites of aspartic proteases (plasmepsin II & IV from Plasmodium falciparum, plasmepsin from Plasmodium vivax, pepsin & cathepsin D from human) are calculated and common pharmacophoric features for favourable binding spots in active sites are extracted in the form of cliques of graphs using inductive logic programming (ILP). The two pharmacophore ensembles are constructed from largest common cliques by imposing size of receptor active site (L) and domain-specific receptor-ligand information (S). The overlap of chemical space between two ensembles and the results of virtual screening of inhibitor database with known activities show that this method can design efficient pharmacophores with no prior ligand information.