scholarly journals Protocol-dependent differences in IC50 values measured in hERG assays occur in a predictable way and can be used to quantify state preference of drug binding

2019 ◽  
Author(s):  
William Lee ◽  
Monique J. Windley ◽  
Matthew D. Perry ◽  
Jamie I. Vandenberg ◽  
Adam P. Hill
Keyword(s):  
State Dependence ◽  
Drug Binding ◽  
Machine Learning Algorithms ◽  
Drug Induced ◽  
Preferential Binding ◽  
State Preference ◽  
Proarrhythmic Risk ◽  
Ic50 Values ◽  
Novel Method ◽  
Open Versus

AbstractCurrent guidelines around preclinical screening for drug-induced arrhythmias require the measurement of the potency of block of Kv11.1 channels as a surrogate for risk. A shortcoming of this approach is that the measured IC50 of Kv11.1 block varies widely depending on the voltage protocol used in electrophysiological assays. In this study, we aimed to investigate the factors that that contribute to these differences and to identify whether it is possible to make predictions about protocol-dependent block that might facilitate comparison of potencies measured using different assaysOur data demonstrate that state preferential binding, together with drug binding kinetics and trapping, is an important determinant of the protocol-dependence of Kv11.1 block. We show for the first time that differences in IC50 measured between protocols occurs in a predictable way, such that machine learning algorithms trained using a selection of simple voltage protocols can indeed predict protocol-dependent potency. Furthermore, we also show that a drug’s preference for binding to the open versus the inactivated state of Kv11.1 can also be inferred from differences in IC50 measured between protocols.Our work therefore identifies how state preferential drug binding is a major determinant of the protocol dependence of IC50 measured in preclinical Kv11.1 assays. It also provides a novel method for quantifying the state dependence of Kv11.1 drug binding that will facilitate the development of more complete models of drug binding to Kv11.1 and improve our understanding of proarrhythmic risk associated with compounds that block Kv11.1.

scholarly journals Protocol-Dependent Differences in IC50 Values Measured in Human Ether-Á-Go-Go–Related Gene Assays Occur in a Predictable Way and Can Be Used to Quantify State Preference of Drug Binding

2019 ◽  
Vol 95 (5) ◽  
pp. 537-550 ◽  
Author(s):  
William Lee ◽  
Monique J. Windley ◽  
Matthew D. Perry ◽  
Jamie I. Vandenberg ◽  
Adam P. Hill
Keyword(s):  
Drug Binding ◽  
Related Gene ◽  
State Preference ◽  
Ic50 Values

Effect of piperacillin on morphology and viability of gram-negative bacteria

1984 ◽  
Vol 42 ◽  
pp. 218-219
Author(s):  
R. H. Liss
Keyword(s):  
Inhibitory Concentration ◽  
Cytoplasmic Membrane ◽  
Drug Binding ◽  
Gram Negative Bacteria ◽  
Bacterial Cells ◽  
Drug Induced ◽  
Penicillin Binding Proteins ◽  
Lactam Antibiotic ◽  
Drug Free ◽  
Tube Dilution

Piperacillip (PIP) is b-[D(-)-α-(4-ethy1-2,3-dioxo-l-piperzinylcar-bonylamino)-α-phenylacetamido]-penicillanate. The broad spectrum semisynthetic β-lactam antibiotic is believed to effect bactericidal activity through its affinity for penicillin-binding proteins (PBPs), enzymes on the bacterial cytoplasmic membrane that control elongation and septation during cell growth and division. The purpose of this study was to correlate penetration and binding of 14C-PIP in bacterial cells with drug-induced lethal changes assessed by microscopic, microbiologic and biochemical methods.The bacteria used were clinical isolates of Escherichia coli and Pseudomonas aeruginosa (Figure 1). Sensitivity to the drug was determined by serial tube dilution in Trypticase Soy Broth (BBL) at an inoculum of 104 organisms/ml; the minimum inhibitory concentration of piperacillin for both bacteria was 1 μg/ml. To assess drug binding to PBPs, the bacteria were incubated with 14C-PIP (5 μg/0.09 μCi/ml); controls, in drug-free medium.

scholarly journals Use of a Consultation Service Following Pharmacogenomic Testing in Psychiatry

CNS Spectrums ◽  
2021 ◽  
Vol 26 (2) ◽  
pp. 179-180
Author(s):  
Daniel Dowd ◽  
David S. Krause
Keyword(s):  
Mental Illness ◽  
Drug Interactions ◽  
Metabolic Pathways ◽  
Patient Characteristics ◽  
Healthcare Resource Utilization ◽  
Machine Learning Algorithms ◽  
Mental Illnesses ◽  
Variable Response ◽  
Drug Induced ◽  
Commercial Laboratory

AbstractBackgroundThere is a plethora of drugs available to psychiatrists for treatment of mental illness, which can vary in efficacy, tolerability, metabolic pathways and drug-drug interactions. Psychotropics are the second most commonly listed therapeutic class mentioned in the FDA’s Table of Pharmacogenomic Biomarkers in Drug Labeling. Pharmacogenomic (PGx) assays are increasingly used in psychiatry to help select safe and appropriate medication for a variety of mental illnesses. Our commercial laboratory offers PGx expert consultations by PharmDs and PhDs to clinician-users. Our database contains valuable information regarding the treatment of a diverse and challenging population.MethodsGenomind offers a PGx assay currently measuring variants of 24 genes relevant for selection of drugs with a mental illness indication. Since 2012 we have analyzed > 250,000 DNA samples. Between 10/18 - 8/20 6,401 reports received a consult. The data contained herein are derived from those consults. Consultants record information on prior meds, reason for failure or intolerability, potential risk-associated or useful drugs based on the genetic variants. Consultants only recommend specific drugs and doses consistent with a published PGx guideline.ResultsThe 5 most commonly discussed genes were SLC6A4, MTHFR, CACNA1C, COMT and BDNF. The 3 most commonly discussed drugs were fluoxetine, lithium and duloxetine. The most common reasons for drug failure were inefficacy and drug induced “agitation, irritability and/or anxiety”. SSRIs were the most common class of discontinued drug; sertraline, escitalopram and fluoxetine were the three most commonly reported discontinuations and were also the 3 most likely to be associated with “no improvement”. Aripiprazole was the most commonly reported discontinued atypical antipsychotic. The providers rated 94% of consultations as extremely or very helpful at the time of consult. An independent validation survey of 128 providers confirmed these ratings, with 96% reporting a rating of “very helpful” or “extremely helpful”. In addition, 94% reported that these consults were superior to PGx consults provided through other laboratories. Patient characteristics captured during consults via a Clinical Global Impressions-Severity (CGI-S) scale revealed that the majority of patients were moderately (54%) or markedly ill (23%). The most frequent symptoms reported were depression, anxiety, insomnia and inattentiveness.DiscussionThe large variety of psychotropic drugs available to providers, and their highly variable response rates, tolerability, capacity for drug-drug interactions and metabolic pathways present a challenge for even expert psychopharmacologists. Consultation with experts in PGx provides additional useful information that may improve outcomes and decrease healthcare resource utilization. This database may provide future opportunities for machine learning algorithms to further inform implications of included gene variants.FundingGenomind, Inc.

Estimation of uncertainty in multi-level in silico models predicting biomarkers of drug-induced proarrhythmic risk

2021 ◽  
Vol 350 ◽  
pp. S64-S65
Author(s):  
K. Kopanska ◽  
J.C. Gómez-Tamayo ◽  
J. Llopis-Lorente ◽  
B.A. Trenor-Gomis ◽  
J. Sáiz ◽  
...  
Keyword(s):  
In Silico ◽  
Drug Induced ◽  
Proarrhythmic Risk ◽  
Multi Level ◽  
In Silico Models ◽  
Estimation Of Uncertainty

scholarly journals Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

2020 ◽  
Author(s):  
Robert Ancuceanu ◽  
Marilena Viorica Hovanet ◽  
Adriana Iuliana Anghel ◽  
Florentina Furtunescu ◽  
Monica Neagu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Injury ◽  
Computational Models ◽  
Liver Toxicity ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Drug Induced ◽  
Reference Drug ◽  
Drug Induced Liver Injury

Drug induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized drugs and candidate drugs and predicting hepatotoxicity from the chemical structure of a substance remains a challenge worth pursuing, being also coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016 a group of researchers from FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans”, DILIrank. This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A number of 78 models with reasonable performance have been selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

scholarly journals Discovery and Design of Novel Small Molecule GSK-3 Inhibitors Targeting the Substrate Binding Site

2020 ◽  
Vol 21 (22) ◽  
pp. 8709
Author(s):  
Ido Rippin ◽  
Netaly Khazanov ◽  
Shirley Ben Joseph ◽  
Tania Kudinov ◽  
Eva Berent ◽  
...  
Keyword(s):  
Binding Site ◽  
Small Molecule ◽  
Substrate Binding ◽  
Pharmacophore Model ◽  
Drug Induced ◽  
Substrate Binding Site ◽  
Zinc Database ◽  
Competitive Inhibitors ◽  
Ic50 Values ◽  
Critical Binding

The serine/threonine kinase, GSK-3, is a promising drug discovery target for treating multiple pathological disorders. Most GSK-3 inhibitors that were developed function as ATP competitive inhibitors, with typical limitations in specificity, safety and drug-induced resistance. In contrast, substrate competitive inhibitors (SCIs), are considered highly selective, and more suitable for clinical practice. The development of SCIs has been largely neglected in the past because the ambiguous, undefined nature of the substrate-binding site makes them difficult to design. In this study, we used our previously described structural models of GSK-3 bound to SCI peptides, to design a pharmacophore model and to virtually screen the “drug-like” Zinc database (~6.3 million compounds). We identified leading hits that interact with critical binding elements in the GSK-3 substrate binding site and are chemically distinct from known GSK-3 inhibitors. Accordingly, novel GSK-3 SCI compounds were designed and synthesized with IC50 values of~1–4 μM. Biological activity of the SCI compound was confirmed in cells and in primary neurons that showed increased β-catenin levels and reduced tau phosphorylation in response to compound treatment. We have generated a new type of small molecule GSK-3 inhibitors and propose to use this strategy to further develop SCIs for other protein kinases.

scholarly journals Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury

2020 ◽  
Vol 8 ◽  
Author(s):  
Ting Li ◽  
Weida Tong ◽  
Ruth Roberts ◽  
Zhichao Liu ◽  
Shraddha Thakkar
Keyword(s):  
Deep Learning ◽  
Liver Injury ◽  
High Throughput ◽  
Permutation Test ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Attrition Rate ◽  
Support Vector ◽  
Drug Induced ◽  
Drug Induced Liver Injury

Drug-induced liver injury (DILI) is one of the most cited reasons for the high drug attrition rate and drug withdrawal from the market. The accumulated large amount of high throughput transcriptomic profiles and advances in deep learning provide an unprecedented opportunity to improve the suboptimal performance of DILI prediction. In this study, we developed an eight-layer Deep Neural Network (DNN) model for DILI prediction using transcriptomic profiles of human cell lines (LINCS L1000 dataset) with the current largest binary DILI annotation data [i.e., DILI severity and toxicity (DILIst)]. The developed models were evaluated by Monte Carlo cross-validation (MCCV), permutation test, and an independent validation (IV) set. The developed DNN model achieved the area under the receiver operating characteristic curve (AUC) of 0.802 and 0.798, and balanced accuracy of 0.741 and 0.721 for training and an IV set, respectively, outperforming the conventional machine learning algorithms, including K-nearest neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). Moreover, the developed DNN model provided a more balanced sensitivity of 0.839 and specificity of 0.603. Besides, we found the developed DNN model had a superior predictive performance for oncology drugs. Also, the functional and network analysis of genes driving the predictions revealed their relevance to the underlying mechanisms of DILI. The proposed DNN model could be a promising tool for early detection of DILI potential in the pre-clinical setting.

scholarly journals Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

2020 ◽  
Vol 21 (6) ◽  
pp. 2114
Author(s):  
Robert Ancuceanu ◽  
Marilena Viorica Hovanet ◽  
Adriana Iuliana Anghel ◽  
Florentina Furtunescu ◽  
Monica Neagu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Injury ◽  
Computational Models ◽  
Liver Toxicity ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Drug Induced ◽  
Reference Drug ◽  
Drug Induced Liver Injury

Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016, a group of researchers from the FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans” (DILIrank). This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A total of 78 models with reasonable performance were selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

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