Molecular Similarity Methods for Predicting Cross-Reactivity With Therapeutic Drug Monitoring Immunoassays

Krasowski, Matthew D.; Siam, Mohamed; Iyer, Manisha; Ekins, Sean

doi:10.1097/ftd.0b013e31819c1b83

Cited by 37 publications

(35 citation statements)

References 29 publications

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“…Cross-reactivity is the failure to target epitope(s) unique to the molecular species being sought for analysis. The fact that both the location and the 3-D structure of most epitope(s) being targeted are unknown means it is difficult to predict species that will cross-react with any particular poly-or monoclonal antibodies [10]. The most desirable antibody is one that captures the least cross-reacting species.…”

Section: Introductionmentioning

confidence: 99%

Screening antibody and immunosorbent selectivity by two‐dimensional liquid chromatography‐MS/MS (2‐D LC‐MS/MS)

Cho

Jung

Regnier

2010

J of Separation Science

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Selectivity of both peptide-and glycan-targeting antibodies was examined by 2-D LC-MS/ MS. Proteins selected from biological extracts immunospecifically in a first chromatography dimension using antibodies immobilized by either covalent coupling or adsorption to protein G were desorbed with a denaturing mobile phase and transferred to a 1.5 mm nonporous particle RP chromatography (NP-RPC) column in a second dimension. Protein peak capacity of the NP-RPC column was approximately 50. Peaks collected from the RPC column were tryptic digested and the peptide fragments were identified by MALDI-MS/ MS. The objective of this analytical strategy was to discriminate between protein antigens and nonantigens through identification of their peptides, leading to an evaluation of the selectivity of antibodies and immunosorbents. Quantification of the relative amount of antigen and nonantigen species captured by immunosorbents was achieved by absorbance, along with the likely capture mechanism. A limitation of the approach was in discriminating between isoforms of an antigen in which neither the antibody nor the LC-MS system targeted the differentiating feature in the isoforms.

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Section: Introductionmentioning

confidence: 99%

Screening antibody and immunosorbent selectivity by two‐dimensional liquid chromatography‐MS/MS (2‐D LC‐MS/MS)

Cho

Jung

Regnier

2010

J of Separation Science

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“…We have previous compared FCFP_6 fingerprints (one of many fingerprint types) and 3D pharmacophores with MDL keys/fingerprints [44,48,49]. The goal of the current study was not to perform an exhausting analysis of fingerprints or similarity measures.…”

Section: Discussionmentioning

confidence: 99%

“…Following an approach used in our previous publications [44,47-49], we have divided the data for these assays in different groups based on degree of cross-reactivity. The exact subdivisions are somewhat arbitrary (especially given the wide and varying numeric cross-reactivity values for these assays) but do provide a relative scale of strong versus weak cross-reactivity.…”

Section: Methodsmentioning

confidence: 99%

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

Krasowski

Ekins

2014

J Cheminform

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BackgroundA challenge for drug of abuse testing is presented by ‘designer drugs’, compounds typically discovered by modifications of existing clinical drug classes such as amphetamines and cannabinoids. Drug of abuse screening immunoassays directed at amphetamine or methamphetamine only detect a small subset of designer amphetamine-like drugs, and those immunoassays designed for tetrahydrocannabinol metabolites generally do not cross-react with synthetic cannabinoids lacking the classic cannabinoid chemical backbone. This suggests complexity in understanding how to detect and identify whether a patient has taken a molecule of one class or another, impacting clinical care.MethodsCross-reactivity data from immunoassays specifically targeting designer amphetamine-like and synthetic cannabinoid drugs was collected from multiple published sources, and virtual chemical libraries for molecular similarity analysis were built. The virtual library for synthetic cannabinoid analysis contained a total of 169 structures, while the virtual library for amphetamine-type stimulants contained 288 compounds. Two-dimensional (2D) similarity for each test compound was compared to the target molecule of the immunoassay undergoing analysis.Results2D similarity differentiated between cross-reactive and non-cross-reactive compounds for immunoassays targeting mephedrone/methcathinone, 3,4-methylenedioxypyrovalerone, benzylpiperazine, mephentermine, and synthetic cannabinoids.ConclusionsIn this study, we applied 2D molecular similarity analysis to the designer amphetamine-type stimulants and synthetic cannabinoids. Similarity calculations can be used to more efficiently decide which drugs and metabolites should be tested in cross-reactivity studies, as well as to design experiments and potentially predict antigens that would lead to immunoassays with cross reactivity for a broader array of designer drugs.

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“…Examples of fragment-based strategies include MACCS (44,45), binary structural keys based on occurrence/counts of up to 166 different chemical features found in a compound; HQSAR (46−48), a 2D method for capturing chiral information based on a molecular hologram hashing algorithm without the requirement for the generation of 3D coordinates; and SKEYS/FRED, a combination of MDL structural key based fingerprints with an evolutionary algorithm (49). …”

Section: Numerical Descriptors Of Chemical Structure For Qsarsmentioning

confidence: 99%

Identification of Metabotropic Glutamate Receptor Subtype 5 Potentiators Using Virtual High-Throughput Screening

Mueller¹,

Rodriguez²,

Dawson

et al. 2010

ACS Chem. Neurosci.

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Selective potentiators of glutamate response at metabotropic glutamate receptor subtype 5 (mGluR5) have exciting potential for the development of novel treatment strategies for schizophrenia. A total of 1,382 compounds with positive allosteric modulation (PAM) of the mGluR5 glutamate response were identified through high-throughput screening (HTS) of a diverse library of 144,475 substances utilizing a functional assay measuring receptor-induced intracellular release of calcium. Primary hits were tested for concentration-dependent activity, and potency data (EC50 values) were used for training artificial neural network (ANN) quantitative structure−activity relationship (QSAR) models that predict biological potency from the chemical structure. While all models were trained to predict EC50, the quality of the models was assessed by using both continuous measures and binary classification. Numerical descriptors of chemical structure were used as input for the machine learning procedure and optimized in an iterative protocol. The ANN models achieved theoretical enrichment ratios of up to 38 for an independent data set not used in training the model. A database of ∼450,000 commercially available drug-like compounds was targeted in a virtual screen. A set of 824 compounds was obtained for testing based on the highest predicted potency values. Biological testing found 28.2% (232/824) of these compounds with various activities at mGluR5 including 177 pure potentiators and 55 partial agonists. These results represent an enrichment factor of 23 for pure potentiation of the mGluR5 glutamate response and 30 for overall mGluR5 modulation activity when compared with those of the original mGluR5 experimental screening data (0.94% hit rate). The active compounds identified contained 72% close derivatives of previously identified PAMs as well as 28% nontrivial derivatives of known active compounds.

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Molecular Similarity Methods for Predicting Cross-Reactivity With Therapeutic Drug Monitoring Immunoassays

Cited by 37 publications

References 29 publications

Screening antibody and immunosorbent selectivity by two‐dimensional liquid chromatography‐MS/MS (2‐D LC‐MS/MS)

Screening antibody and immunosorbent selectivity by two‐dimensional liquid chromatography‐MS/MS (2‐D LC‐MS/MS)

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

Identification of Metabotropic Glutamate Receptor Subtype 5 Potentiators Using Virtual High-Throughput Screening

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