Prediction of milk/plasma concentration ratios of drugs and environmental pollutants

Abraham, Michael H.; Gil-Lostes, Javier; Fatemi, M.

doi:10.1016/j.ejmech.2009.01.009

Cited by 28 publications

(38 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Protein binding is the strongest predictor of drug transfer in BM, 41 which explains lower levels of PIs penetrating BM compared with the other classes of drug (∼30% protein binding for zidovudine and lamivudine compared with >90% for the PIs). 42 Maternal covariates influencing the variability in BM elimination of ARVs are poorly defined, but may include clinical states affecting circulating plasma proteins, such as intercurrent illness and poor nutrition.…”

Section: Discussionmentioning

confidence: 99%

Is infant exposure to antiretroviral drugs during breastfeeding quantitatively important? A systematic review and meta-analysis of pharmacokinetic studies

Waitt

Garner

Bonnett

et al. 2015

Journal of Antimicrobial Chemotherapy

View full text Add to dashboard Cite

ObjectivesThe objectives of this study were to summarize antiretroviral drug concentrations in breast milk (BM) and exposure of breast-fed infants.MethodsThis was a systematic review of pharmacokinetic studies of HIV-positive women taking antiretrovirals that measured drugs in BM. The quality of pharmacokinetic and laboratory methods was assessed using pre-defined criteria. Pooled ratios and 95% CIs were calculated using the generalized inverse variance method and heterogeneity was estimated by the I2 statistic. PubMed Central, SCOPUS and LactMed databases were searched. No date or language restrictions were applied. Searches were conducted up to 10 November 2014. Clinical relevance was estimated by comparing ingested dose with the recommended therapeutic dose for each drug.ResultsTwenty-four studies were included. There was substantial variability in the clinical and laboratory methods used and in reported results. Relative to maternal plasma (MP), NRTIs accumulate in BM, with BM : MP ratios (95% CI estimates) from 0.89 to 1.21 (14 studies, 1159 paired BM and MP samples). NNRTI estimates were from 0.71 to 0.94 (17 studies, 965 paired samples) and PI estimates were from 0.17 to 0.21 (8 studies, 477 paired samples). Relative to the recommended paediatric doses, a breast-fed infant may ingest 8.4% (95% CI 1.9–15.0), 12.5% (95% CI 2.6–22.3) and 1.1% (95% CI 0–3.6) of lamivudine, nevirapine and efavirenz, respectively, via BM.ConclusionsTransfer to untreated infants appears quantitatively important for some NRTIs and NNRTIs. The pharmacokinetic methods varied widely and we propose standards for the design, analysis and reporting of future pharmacokinetic studies of drug transfer during breastfeeding.

show abstract

Section: Discussionmentioning

confidence: 99%

Is infant exposure to antiretroviral drugs during breastfeeding quantitatively important? A systematic review and meta-analysis of pharmacokinetic studies

Waitt

Garner

Bonnett

et al. 2015

Journal of Antimicrobial Chemotherapy

View full text Add to dashboard Cite

show abstract

“…However, it is noteworthy the influence of other weights such as Abraham indices Ab-R 2 and Ab-logL 16 . Both have been successfully applied before to the field of linear solvation energy relationships (LSERs) to predict the solvation properties considering the effects of solvent-solute interactions in physicochemical and biochemical phenomena like, for example, transport processes involving transfer of solutes from the gas phase to a condensed phase or chromatographic retention characteristic [65][66][67]. The Ab-R 2 is a function of the refractive index and the molar volume of the solute in a direct relation.…”

Section: Resultsmentioning

confidence: 99%

Topological sub-structural molecular design (TOPS-MODE): a useful tool to explore key fragments of human $$\mathbf{A}_{3}$$ A 3 adenosine receptor ligands

et al. 2015

View full text Add to dashboard Cite

Adenosine regulates tissue function by activating four G-protein-coupled adenosine receptors (ARs). Selective agonists and antagonists for A3 ARs have been investigated for the treatment of a variety of immune disorders, cancer, brain, and heart ischemic conditions. We herein present a QSAR study based on a Topological sub-structural molecular design (TOPS-MODE) approach, intended to predict the A3 ARs of a diverse dataset of 124 (94 training set/ 30 prediction set) adenosine derivatives. The final model showed good fit and predictive capability, displaying 85.1 % of the experimental variance. The TOPS-MODE approach afforded a better understanding and interpretation of the developed model based on the useful information extracted from the analysis of the contribution of different molecular fragments to the affinity.

show abstract

“…The only statistic they gave was an 'accuracy' of 90.48 %. Also, Abraham et al [21] developed QSAR models for M/P ratio with a large data set consisting of 179 drugs and hydrophobic environmental pollutants. They developed a nonlinear ANN model using five linear free energy relationship (LFER) parameters as inputs.…”

Section: Ei ð%þ ¼ 100 â ðM=pþ â A=infantdrugclearance ð1þmentioning

confidence: 99%

“…[20][21][22] The advantage of including such a wide range of molecules for development of quantitative models is that the models encompass a wide range of applicability domain and hence can be successfully utilized for prediction of a variety of untested molecules. The data were assigned as high risk (H) (M/P > 1) and low risk (L) (M/P < 1) drugs as proposed by Malone et al [25] for classification based QSTR model.…”

Section: Datasetmentioning

confidence: 99%

Prediction of Milk/Plasma Concentration Ratios of Drugs and Environmental Pollutants Using In Silico Tools: Classification and Regression Based QSARs and Pharmacophore Mapping

Kar

Roy

2013

Molecular Informatics

View full text Add to dashboard Cite

A large set of 185 compounds with diverse molecular structures and different mechanisms of therapeutic actions was used to develop and validate statistically significant classification and regression based QSTR models for predicting partitioning of drugs/chemicals into breast milk. Pharmacophore mapping was also carried out which showed four important features required for lower risk of secretion into milk: (i) hydrophobic group (HYD), (ii) ring aromatic group (RA), (iii) negative ionizable (NegIon) and (iv) hydrogen bond donor (HBA). QSTR and pharmacophore models were rigorously validated internally as well as externally to check the possibilities of any chance correlation and judge the predictive potential of the models. Pharmacological distribution diagrams (PDDs) were used for the classification model as a visualizing technique for the identification and selection of chemicals with lower partitioning into milk. Our in silico models enable to identify the essential structural attributes and quantify the prime molecular pre-requisites which were chiefly responsible for secretion into milk. The developed models were also implemented to screen milk/plasma partitioning potential for a huge number DrugBank database (http://www.drugbank.ca/) compounds.

show abstract

Prediction of milk/plasma concentration ratios of drugs and environmental pollutants

Cited by 28 publications

References 76 publications

Is infant exposure to antiretroviral drugs during breastfeeding quantitatively important? A systematic review and meta-analysis of pharmacokinetic studies

Is infant exposure to antiretroviral drugs during breastfeeding quantitatively important? A systematic review and meta-analysis of pharmacokinetic studies

Topological sub-structural molecular design (TOPS-MODE): a useful tool to explore key fragments of human $$\mathbf{A}_{3}$$ A 3 adenosine receptor ligands

Prediction of Milk/Plasma Concentration Ratios of Drugs and Environmental Pollutants Using In Silico Tools: Classification and Regression Based QSARs and Pharmacophore Mapping

Contact Info

Product

Resources

About