A new biodegradation prediction model specific to petroleum hydrocarbons

Howard, Philip H.; Meylan, William M.; Aronson, Dallas; Stiteler, William; Tunkel, Jay; Comber, Mike; Parkerton, Thomas F.

doi:10.1897/04-453r.1

Cited by 66 publications

(50 citation statements)

References 46 publications

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“…The probability of biodegradation attempts to combine both oxidative and biotransformation susceptibility of the structure to give an estimate of overall persistence. P[BD] estimates are based upon molecular fragments [46–48] and in our investigation showed only moderate correlation with MRTD in both multiple linear regression and neural network models. Each of the models was evaluated for predictive accuracy and by statistical comparison.…”

Section: Discussioncontrasting

confidence: 54%

Predictive Models for Maximum Recommended Therapeutic Dose of Antiretroviral Drugs

Branham

Ross

Govender

2012

Computational and Mathematical Methods in Medicine

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A novel method for predicting maximum recommended therapeutic dose (MRTD) is presented using quantitative structure property relationships (QSPRs) and artificial neural networks (ANNs). MRTD data of 31 structurally diverse Antiretroviral drugs (ARVs) were collected from FDA MRTD Database or package inserts. Molecular property descriptors of each compound, that is, molecular mass, aqueous solubility, lipophilicity, biotransformation half life, oxidation half life, and biodegradation probability were calculated from their SMILES codes. A training set (n = 23) was used to construct multiple linear regression and back propagation neural network models. The models were validated using an external test set (n = 8) which demonstrated that MRTD values may be predicted with reasonable accuracy. Model predictability was described by root mean squared errors (RMSEs), Kendall's correlation coefficients (tau), P-values, and Bland Altman plots for method comparisons. MRTD was predicted by a 6-3-1 neural network model (RMSE = 13.67, tau = 0.643, P = 0.035) more accurately than by the multiple linear regression (RMSE = 27.27, tau = 0.714, P = 0.019) model. Both models illustrated a moderate correlation between aqueous solubility of antiretroviral drugs and maximum therapeutic dose. MRTD prediction may assist in the design of safer, more effective treatments for HIV infection.

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Section: Discussioncontrasting

confidence: 54%

Predictive Models for Maximum Recommended Therapeutic Dose of Antiretroviral Drugs

Branham

Ross

Govender

2012

Computational and Mathematical Methods in Medicine

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“…BioHCwin is a model that predicts the primary biodegradation half-lives of hydrocarbons under environmentally relevant conditions [33]. Using the same fragment methodology as BioWin, this model was developed to expand the number of fragments to describe more fully petroleum hydrocarbons.…”

Section: Biodegradation Estimation Methodsmentioning

confidence: 99%

“…Based on the coefficient values [33], the ranking of BioHCwin fragments contributing to longer predicted half-lives is carbon with four single bonds and no hydrogens % fused acyclic ring > CH linear % polycyclic methyl group >> remaining fragments in Table 3. Consequently, a-cedrene, a-gurjunene, longifolene, and (À)-thujopsene, which are assigned three polycyclic methyl groups and one or more quaternary carbons, have estimated half-lives of !180 d and are screened potentially vP ( Table 2, columns 12-14).…”

Section: Biodegradation Estimationsmentioning

confidence: 99%

Persistency assessment and aerobic biodegradation of selected cyclic sesquiterpenes present in essential oils

Jenner¹,

Kreutzer

Racine³

2011

Enviro Toxic and Chemistry

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Sesquiterpenes are ubiquitous in essential oils but an assessment of their environmental behavior is still required for their use as components of natural fragrance ingredients and oral care flavors. Persistency plays a key role in hazard and risk assessment, but the current knowledge on the biodegradation of sesquiterpenes in the aquatic environment is limited. This could have important consequences for the persistent, bioaccumulative and toxic (PBT) assessment of essential oils because most of the sesquiterpene components have a log K(OW) of >4.5 and are identified as potentially bioaccumulating according to REACH screening criteria. In the present study, a persistency screening assessment was conducted on 11 cyclic sesquiterpenes selected from 10 different families of sesquiterpenes characterized by their carbon skeleton. Current biodegradation prediction models (BioWin™, BioHCwin, and Catalogic) were found to be of limited use because most of the sesquiterpenes studied were outside the structural domain of the models. Aerobic biodegradation was measured in a standard or prolonged Organisation for Economic Co-operation and Development (OECD) 301F Manometric Respirometry test for ready biodegradability. α-Bisabolol, α-humulene, β-caryophyllene, α-cedrene, cedrol, longifolene, and δ-cadinene exceeded the pass level of 60% degradation and can be regarded as not persistent. Alpha-gurjunene, himachalenes (α, β, γ), and (-)-thujopsene almost achieved the pass level reaching between 51% and 56% ultimate biodegradation. Although germacrene D only achieved 24% ultimate biodegradation, specific analysis at the end of the test did indicate complete primary degradation. Given that the shape of the biodegradation curves indicates poor bioavailability and ready biodegradability tests are very stringent, it is expected that all the sesquiterpenes tested in the present study would be degraded under environmental conditions.

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“…Quantitative measures of biodegradability (half-lives or rate constants) would probably be more useful in risk assessment and are essential for multimedia fate/transport modeling, but no broadly applicable models exist at present. A new model specific to petroleum hydrocarbons predicts half-life for this broad class of chemicals, 108 but users have not had much time to test it. However, the dearth of quantitative models matters little because an expression of relative biodegradability usually is sufficient in chemical design.…”

Section: Approaches To Modelingmentioning

confidence: 99%