Predicting plasma protein binding of drugs: a new approach

Kratochwil, Nicole A.; Huber, Walter; Müller, Francis; Kansy, Manfred; Gerber, Paul R.

doi:10.1016/s0006-2952(02)01074-2

Cited by 532 publications

(457 citation statements)

References 172 publications

Supporting

Mentioning

432

Contrasting

Unclassified

Order By: Relevance

“…Although plasma protein binding of drugs has been studied for almost 100 years, accurate prediction of this ADME parameter continues to be problematic. 25 There are numerous in vitro methods for the determination of protein binding, including equilibrium dialysis, dynamic dialysis, ultrafiltration, ultracentrifugation, exclusion chromatography, and circular dichroism. Because the binding of drugs to plasma proteins is an important factor in determining their pharmacokinetics and pharmacological effects, plasma protein binding is routinely determined in vitro for drugs in discovery and development.…”

Section: Protein Bindingmentioning

confidence: 99%

Drug metabolism and pharmacokinetics, the blood-brain barrier, and central nervous system drug discovery

et al. 2005

View full text Add to dashboard Cite

Summary:The worldwide market for therapies for CNS disorders is worth more than $50 billion and is set to grow substantially in the years ahead. This is because: 1) the incidence of many CNS disorders (e.g., Alzheimer's disease, stroke, and Parkinson's disease) increase exponentially after age 65 and 2) the number of people in the world over 65 is about to increase sharply because of a marked rise in fertility after World War II. However, CNS research and development are associated with significant challenges: it takes longer to get a CNS drug to market (12-16 years) compared with a non-CNS drug (10 -12 years) and there is a higher attrition rate for CNS drug candidates than for non-CNS drug candidates. This is attributable to a variety of factors, including the complexity of the brain, the liability of CNS drugs to cause CNS side effects, and the requirement of CNS drugs to cross the blood-brain barrier (BBB). This review focuses on BBB penetration, along with pharmacokinetics and drug metabolism, in the process of the discovery and development of safe and effective medicines for CNS disorders.

show abstract

Section: Protein Bindingmentioning

confidence: 99%

Drug metabolism and pharmacokinetics, the blood-brain barrier, and central nervous system drug discovery

et al. 2005

View full text Add to dashboard Cite

show abstract

“…Although in most cases lipophilicity was identified as the main factor favoring PPB, there are also conflicting findings. Kratochwil didn't find sound correlation between lipophilicity and PPB in a dataset of diverse drugs and stated that bases and acids should be treated separately because the influence of lipophilicity on HSA binding is larger for acids than for bases (27). Yamazaki developed statistically significant non-linear relationship between %PPB and logD 7.4 for a dataset of 90 basic and neutral drugs, but not for acidic drugs and for diverse dataset (28).…”

Section: Introductionmentioning

confidence: 99%

Quantitative Structure – Pharmacokinetics Relationships for Plasma Protein Binding of Basic Drugs

Zhivkova

2017

J Pharm Pharm Sci

View full text Add to dashboard Cite

Purpose. Binding of drugs to plasma proteins is a common physiological occurrence which may have a profound effect on both pharmacokinetics and pharmacodynamics. The early prediction of plasma protein binding (PPB) of new drug candidates is an important step in drug development process. The present study is focused on the development of quantitative structure -pharmacokinetics relationship (QSPkR) for the negative logarithm of the free fraction of the drug in plasma (pf u ) of basic drugs. Methods. A dataset includes 220 basic drugs, which chemical structures are encoded by 176 descriptors. Genetic algorithm, stepwise regression and multiple linear regression are used for variable selection and model development. Predictive ability of the model is assessed by internal and external validation. Results. A simple, significant, interpretable and predictive QSPkR model is constructed for pf u of basic drugs. It is able to predict 59% of the drugs from an external validation set within the 2-fold error of the experimental values with squared correlation coefficient of prediction 0.532, geometric mean fold error (GMFE) 1.94 and mean absolute error (MAE) 0.17. Conclusions. PPB of basic drugs is favored by the lipophilicity, the presence of aromatic C-atoms (either non-substituted, or involved in bridged aromatic systems) and molecular volume. The fraction ionized as a base f B and the presence of quaternary Catoms contribute negatively to PPB. A short checklist of criteria for high PPB is defined, and an empirical rule for distinguishing between low, high and very high plasma protein binders is proposed based. This rule allows correct classification of 69% of the very high binders, 71% of the high binders and 91% of the low binders in plasma.

show abstract

“…This would allow speeding up of the design of new compounds with appropriate BSA binding properties and therefore the optimization of the pharmacokinetics. This approach has been successfully utilized for the prediction of many physical properties, and it is one of the main approaches in the area of binding affinity prediction [18][19][20]. To the best of our knowledge, there are no QSPR studies focused on the prediction of the binding constants of active traditional Chinese medicine constituents measured by fluorescence quenching until now.…”

Section: Central European Journal Of Chemistrymentioning

confidence: 99%

Quantitative structure-property relationship study on the determination of binding constant by fluorescence quenching

et al. 2009

View full text Add to dashboard Cite

Models to predict binding constant (logK) to bovine serum albumin (BSA) should be very useful in the pharmaceutical industry to help speed up the design of new compounds, especially as far as pharmacokinetics is concerned. We present here an extensive list of logK binding constants for thirty-five compounds to BSA determined by florescence quenching from the literature. These data have allowed us the derivation of a quantitative structure-property relationship (QSPR) model to predict binding constants to BSA of compounds on the basis of their structure. A stepwise multiple linear regression (MLR) was performed to build the model. The statistical parameter provided by the MLR model (R = 0.9200, RMS = 0.3305) indicated satisfactory stability and predictive ability for the model. Using florescence quenching spectroscopy, we also experimentally determined the binding constants to BSA for two bioactive components in traditional Chinese medicines. Using the proposed model it was possible to predict the binding constants for each, which were in good agreement with the experimental results. This QSPR approach can contribute to a better understanding of structural factors of the compounds responsible for drug-protein interactions, and be useful in predicting the binding constants of other compounds.© Versita Warsaw and Springer-Verlag Berlin Heidelberg.

show abstract

Predicting plasma protein binding of drugs: a new approach

Cited by 532 publications

References 172 publications

Drug metabolism and pharmacokinetics, the blood-brain barrier, and central nervous system drug discovery

Drug metabolism and pharmacokinetics, the blood-brain barrier, and central nervous system drug discovery

Quantitative Structure – Pharmacokinetics Relationships for Plasma Protein Binding of Basic Drugs

Quantitative structure-property relationship study on the determination of binding constant by fluorescence quenching

Contact Info

Product

Resources

About