Chemoinformatic methods for predicting interference in drug of abuse/toxicology immunoassays.
Chemoinformatic methods for predicting interference in drug of abuse/toxicology immunoassays.
复制标题
用于预测干扰滥用/毒理学免疫测定药物的化学信息学方法。
DOI：
10.1373/clinchem.2008.118638
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发表时间：
2009-06
影响因子：
9.3
通讯作者：
Ekins, Sean
中科院分区：
文献类型：
--
作者：
Krasowski, Matthew D.;Siam, Mohamed G.;Iyer, Manisha;Pizon, Anthony F.;Giannoutsos, Spiros;Ekins, Sean
用于常规滥用药物（DOA）和毒理学筛选的免疫测定可能受到能够以与靶分子类似的方式结合抗体的交叉反应化合物的限制。迄今为止，很少有系统的研究使用计算工具来预测交叉反应的化合物。常用的分子相似性方法能够计算各种化合物（处方药和非处方药、非法药物和临床显著代谢物）与DOA/毒理学筛选测定的靶分子的结构相似性。我们利用不同的分子描述符（MDL公钥，功能类指纹，药效团指纹）和Tanimoto相似系数。然后将这些数据与市售用于体外诊断的免疫测定试剂盒包装说明书中的交叉反应性数据进行比较。检测了先前未检测的化合物，这些化合物被预测具有高交叉反应性概率。使用MDL公钥和Tanimoto相似性系数计算的分子相似性表明，交叉反应性和非交叉反应性化合物之间存在强有力的统计学显著性分离。这通过基于计算预测的额外交叉反应性化合物的发现进行了实验验证。所采用的计算方法适合于快速筛选药物，代谢物和内源性分子的数据库，并可能是有用的，以确定交叉反应的分子，否则会怀疑。这些方法还可能具有将交叉反应性测试集中在与靶分子具有高度相似性的化合物上以及限制测试具有低相似性和与测定交叉反应的概率非常低的化合物的价值。
Immunoassays used for routine drug of abuse (DOA) and toxicology screening may be limited by cross-reacting compounds able to bind to the antibodies in a manner similar to the target molecule(s). To date, there has been little systematic investigation using computational tools to predict cross-reactive compounds. Commonly used molecular similarity methods enabled calculation of structural similarity for a wide range of compounds (prescription and over-the-counter medications, illicit drugs, and clinically significant metabolites) to the target molecules of DOA/toxicology screening assays. We utilized different molecular descriptors (MDL public keys, functional class fingerprints, and pharmacophore fingerprints) and the Tanimoto similarity coefficient. These data were then compared with cross-reactivity data in the package inserts of immunoassays marketed for in vitro diagnostic use. Previously untested compounds that were predicted to have a high probability of cross-reactivity were tested. Molecular similarity calculated using MDL public keys and the Tanimoto similarity coefficient showed a strong and statistically significant separation between cross-reactive and non-cross-reactive compounds. This was validated experimentally by discovery of additional cross-reactive compounds based on computational predictions. The computational methods employed are amenable towards rapid screening of databases of drugs, metabolites, and endogenous molecules, and may be useful for identifying cross-reactive molecules that would be otherwise unsuspected. These methods may also have value in focusing cross-reactivity testing on compounds with high similarity to the target molecule(s) and limiting testing of compounds with low similarity and very low probability of cross-reacting with the assay.
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DOI：
10.1073/pnas.90.21.10310
发表时间：
1993-11-01
影响因子：
11.1
作者：
JEFFREY, PD;STRONG, RK;SHERIFF, S
通讯作者：
SHERIFF, S
影响因子：
2.9
作者：
Caravati, EM;Juenke, JM;Anderson, KT
通讯作者：
Anderson, KT
影响因子：
2.5
作者：
Fitzgerald, RL;Herold, DA
通讯作者：
Herold, DA
DOI：
10.1021/ci034231b
发表时间：
2004-05-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
Hert, J;Willett, P;Wilton, DJ
通讯作者：
Wilton, DJ
影响因子：
2.5
作者：
ELSOHLY, MA;JONES, AB;ELSOHLY, HN
通讯作者：
ELSOHLY, HN