scholarly journals Pregabalin P oisoning: Evaluation of Dose‐Toxicity Relationship

2021 ◽  
Author(s):  
Saskia J. Rietjens ◽  
Maaike A. Sikma ◽  
Claudine C. Hunault ◽  
Dylan W. Lange ◽  
Laura Hondebrink
Keyword(s):  
Toxicity Relationship

scholarly journals Correlation between the structure and skin permeability of compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu
Keyword(s):  
Large Data ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Skin Permeability ◽  
Data Set ◽  
Test Set ◽  
Svm Algorithm ◽  
Svm Model ◽  
Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

Quantitative concentration-toxicity relationship for the injury of rat thymocytes by chemical compounds used in inter-laboratory toxicity ring-tests

1994 ◽  
Vol 8 (4) ◽  
pp. 831-834 ◽  
Author(s):  
M.C. Lopes ◽  
L. Guilhermino ◽  
A. Donato ◽  
L. Silveira ◽  
A.M.V.M. Soares ◽  
...  
Keyword(s):  
Chemical Compounds ◽  
Toxicity Relationship

Thiophenes as mosquito larvicides: Structure-toxicity relationship analysis

1991 ◽  
Vol 41 (1) ◽  
pp. 89-100 ◽  
Author(s):  
Robin J. Marles ◽  
R.Lilia Compadre ◽  
Cesar M. Compadre ◽  
Chantal Soucy-Breau ◽  
Robert W. Redmond ◽  
...  
Keyword(s):  
Relationship Analysis ◽  
Mosquito Larvicides ◽  
Structure Toxicity Relationship ◽  
Toxicity Relationship

scholarly journals Exposure‐toxicity relationship of cabozantinib in patients with renal cell cancer and salivary gland cancer

2021 ◽  
Author(s):  
Stefanie D. Krens ◽  
Wim Boxtel ◽  
Maike J. M. Uijen ◽  
Frank G. A. Jansman ◽  
Ingrid M. E. Desar ◽  
...  
Keyword(s):  
Salivary Gland ◽  
Renal Cell Cancer ◽  
Renal Cell ◽  
Cell Cancer ◽  
Salivary Gland Cancer ◽  
Relationship Of ◽  
Toxicity Relationship

scholarly journals Using an Interaction Parameter in Model-Based Phase I Trials for Combination Treatments? A Simulation Study

2021 ◽  
Vol 18 (1) ◽  
pp. 345
Author(s):  
Pavel Mozgunov ◽  
Rochelle Knight ◽  
Helen Barnett ◽  
Thomas Jaki
Keyword(s):  
Phase I ◽  
Interaction Parameter ◽  
Simulation Study ◽  
Small Sample Size ◽  
Small Sample ◽  
Dose Finding ◽  
Phase I Trials ◽  
Model Based ◽  
Combination Toxicity ◽  
Toxicity Relationship

There is growing interest in Phase I dose-finding studies studying several doses of more than one agent simultaneously. A number of combination dose-finding designs were recently proposed to guide escalation/de-escalation decisions during the trials. The majority of these proposals are model-based: a parametric combination-toxicity relationship is fitted as data accumulates. Various parameter shapes were considered but the unifying theme for many of these is that typically between 4 and 6 parameters are to be estimated. While more parameters allow for more flexible modelling of the combination-toxicity relationship, this is a challenging estimation problem given the typically small sample size in Phase I trials of between 20 and 60 patients. These concerns gave raise to an ongoing debate whether including more parameters into combination-toxicity model leads to more accurate combination selection. In this work, we extensively study two variants of a 4-parameter logistic model with reduced number of parameters to investigate the effect of modelling assumptions. A framework to calibrate the prior distributions for a given parametric model is proposed to allow for fair comparisons. Via a comprehensive simulation study, we have found that the inclusion of the interaction parameter between two compounds does not provide any benefit in terms of the accuracy of selection, on average, but is found to result in fewer patients allocated to the target combination during the trial.

scholarly journals HUBUNGAN KUANTITATIF STRUKTUR TOKSISITAS : REVIEW

2020 ◽  
Vol 3 (2) ◽  
pp. 107-126
Author(s):  
Purwaniati Purwaniati
Keyword(s):  
In Silico ◽  
Toxicity Data ◽  
Quantitative Structure ◽  
Training Set ◽  
Drug Discovery And Development ◽  
Independent Variables ◽  
Drug Molecules ◽  
Structure Toxicity Relationship ◽  
Toxicity Relationship ◽  
In Silico Methods

AbstrakProses penemuan dan pengembangan obat merupakan proses panjang yang memerlukan banyak waktu dan biaya. Ada banyak calon molekul obat yang gagal mencapai pasaran karena alasan toksisitasnya yang tinggi, sehingga harus dapat diidentifikasi sedini mungkin. Hubungan kuantitatif struktur toksisitas (HKST) merupakan salah satu metode in silico yang cukup tangguh untuk memprediksi toksisitas. HKST merupakan persamaan matematis yang dibentuk dari variabel data endpoint toksisitas seperti LD50 sebagai variabel terikat dan sejumlah deskriptor sebagai variable bebas yang dihitung dari senyawa-senyawa dalam training set. Persamaan HKST kemudian digunakan untuk memprediksi toksisitas senyawa baru.Kata kunci : toksisitas, hubungan kuantitatif struktur toksisitas (HKST)AbstractThe process of drug discovery and development is a long process that requires a lot of time and costly. There are many prospective drug molecules that fail to reach the market due to high toxicity reasons, so they must be identified as early as possible. The quantitative structure toxicity relationship  (QSTR) is one of the in silico methods that is strong enough to predict toxicity. QSTR is a mathematical equation formed from endpoint toxicity data variables such as LD50 as a bound variable and a number of descriptors as independent variables calculated from the compounds in the training set. The QSTR equation is then used to predict the toxicity of new compounds.Keywords: toxicity, quantitative structure toxicity relationship (QSTR)

Toxicity of Halogen, Sulfur and Chlorinated Aromatic Compounds

2011 ◽  
Vol 1 (1) ◽  
pp. 61-74 ◽  
Author(s):  
Ashutosh Kumar Gupta ◽  
Arindam Chakraborty ◽  
Santanab Giri ◽  
Venkatesan Subramanian ◽  
Pratim Chattaraj
Keyword(s):  
Aromatic Compounds ◽  
Density Functional ◽  
Inorganic Compounds ◽  
Dft Method ◽  
Qsar Model ◽  
Electrophilicity Index ◽  
Conceptual Density Functional Theory ◽  
Chlorinated Aromatic Compounds ◽  
Structure Toxicity Relationship ◽  
Toxicity Relationship

In this paper, quantitative–structure–toxicity–relationship (QSTR) models are developed for predicting the toxicity of halogen, sulfur and chlorinated aromatic compounds. Two sets of compounds, containing mainly halogen and sulfur inorganic compounds in the first set and chlorinated aromatic compounds in the second, are investigated for their toxicity level with the aid of the conceptual Density Functional Theory (DFT) method. Both sets are tested with the conventional density functional descriptors and with a newly proposed net electrophilicity descriptor. Associated R2, R2CV and R2adj values reveal that in the first set, the proposed net electrophilicity descriptor (??±) provides the best result, whereas in the second set, electrophilicity index (?) and a newly proposed descriptor, net electrophilicity index (??±) provide a comparable performance. The potential of net electrophilicity index to act as descriptor in development of QSAR model is also discussed.

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