General Linearized Biexponential Model for QSAR Data Showing Bilinear-Type Distribution

Buchwald, Péter

doi:10.1002/jps.20438

Cited by 31 publications

(50 citation statements)

References 77 publications

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“…These data were used for nonlinear regression with fitting of the bilinear function performed as described earlier (Buchwald, 2005(Buchwald, , 2007. Bilinear fittings were performed with the general LinBiExp model that can fit any bilinear-type data, y, i.e., data that show a maximum, a minimum, or just a ratechange around a given x value but tend to show good linearity away from this (Buchwald, 2005(Buchwald, , 2007.…”

Section: Methodsmentioning

confidence: 99%

“…Bilinear fittings were performed with the general LinBiExp model that can fit any bilinear-type data, y, i.e., data that show a maximum, a minimum, or just a ratechange around a given x value but tend to show good linearity away from this (Buchwald, 2005(Buchwald, , 2007. LinBiExp uses a functional form consisting of the logarithm of the sum of two exponentials to obtain a bilinear functionality:…”

Section: Methodsmentioning

confidence: 99%

“…For QSAR-type analyses, we found LinBiExp to be most useful in fitting decimal log-scaled concentration data (e.g., log 1/IC 50 , log 1/ED 50 , log 1/K d ) as a function of molecular size (Buchwald, 2005(Buchwald, , 2008Buchwald and Bodor, 2006;Buchwald and Yamashita, 2014). While we found molecular volume (V) as a particularly useful three-dimensional size descriptor and used it in various previous bilinear-type fittings, here we used the more convenient and readily available molecular weight as the independent variable (i.e., x = MW in eq.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Our approach is based on the linearized biexponential (LinBiExp) model that allows bilinear dependence on two sides of a smooth transition region positioned at a turning point (Buchwald, 2005(Buchwald, , 2007. Such an approach already proved useful in a much more limited quantitative structure-activity relationship (QSAR) for CYP3A4 (Buchwald and Yamashita, 2014), as well as in modeling several other pharmaceutically relevant activities (Buchwald, 2005(Buchwald, , 2008Buchwald and Bodor, 2006). This approach allowed development of a QSAR equation that accounted for close to 65% of the variability in the CYP3A4-inhibitory activity of more than 70 structurally diverse compounds with only two main descriptors, molecular size and an indicator variable for the presence of triazole/imidazole moieties (Buchwald and Yamashita, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Activity-Limiting Role of Molecular Size: Size-Dependency of Maximum Activity for P450 Inhibition as Revealed by qHTS Data

Buchwald¹

2014

Drug Metab Dispos

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Analysis of a large number of data on cytochrome P450 (P450) inhibition obtained from quantitative high-throughput screening assays from the PubChem BioAssay Database clearly indicates that molecular size has an important activity-limiting role for datasets focused on drug-like compounds (PubChem BioAssay Identifier [AID] 1851) as well as for datasets also incorporating a wider range of environmental chemicals (AIDs 410, 899, 883, 891, and 884). Maximum inhibitory activity increases with size for small enough structures then plateaus and begins to show a decreasing trend for larger structures. Log-scaled maximum median inhibitory concentration (pIC 50 ) as a function of molecular size could be fitted well with a bilinear model (LinBiExp), and the shape of the curve is quite similar across five P450 isozymes (CYP1A2, 2C9, 2C19, 2D6, and 3A4) with a turning-point of maximum inhibition around 300-500 Da. While the present size-based approach cannot account for the variability of activity in general, using data for a very large number of compounds, it still provides an intuitive interpretation of the maximum P450-inhibitory activity obtainable for a given molecular size and highlights the presence of an "optimum" size range.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Activity-Limiting Role of Molecular Size: Size-Dependency of Maximum Activity for P450 Inhibition as Revealed by qHTS Data

Buchwald¹

2014

Drug Metab Dispos

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Retrometabolism‐Based Drug Design and Targeting

Bodor

Buchwald

2010

Burger's Medicinal Chemistry and Drug Discovery

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Retrometabolic drug design (RMDD) approaches are systematic methodologies aimed to design safe compounds with an improved therapeutic index. RMDDs thoroughly integrate structure– activity and structure‐metabolism considerations into the entire design process and incorporate two major concepts aimed to design soft drugs and chemical delivery systems, respectively. Soft drugs (SDs) are new, active therapeutic agents designed to undergo predictable metabolism resulting in inactive metabolites after exerting their desired therapeutic effect(s). Chemical delivery systems (CDSs) are biologically inert molecules that provide enhanced and targeted delivery of an active drug to a particular organ or site through a designed sequential metabolism that involves several steps. Here, we present a comprehensive review including a general overview of the RMDD principles and a large number of specific examples from various pharmacological areas, including many recent developments, to illustrate RMDD concepts. Whenever possible, the rationale for the design as well as the pharmacokinetic and pharmacodynamic properties of the new therapeutic agents are briefly summarized and compared to those of the other compounds used in the same field.

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Anti‐Inflammatory Glucocorticoids

Avery

Chittiboyina

2010

Burger's Medicinal Chemistry and Drug Discovery

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Glucocorticoids are the first line medication used in the treatment of afflictions ranging from the moderate skin rash to severe acute inflammatory disorders. They exert their activity through binding to the glucocorticoid receptor, a ligand‐dependent transcription factor, and result in either activation or repression of a large set of glucocorticoid responsive genes. The glucocorticoid receptor ( GR ) is a cytoplasmic protein of complex structure that is associated with various heat shock proteins (e.g., hsp 90). The desired immunosuppressive effect is apparently due largely to the downregulation of a variety of proinflammatory factors, whereas adverse reactions such as corticoid‐induced diabetes and osteoporosis could be connected to the inappropriate activation of genes involved in the control of metabolic processes. The discovery of selective receptor modulators, which maintain beneficial therapeutic activity together with a diminished risk of side effects, focuses on ligands that lead to repression rather than activation of genes targeted by the glucocorticoid receptor. Current drug‐screening programs have yielded a number of molecules including steroidal as well as nonsteroidal compounds, which preferentially induce receptor‐mediated repression.

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General Linearized Biexponential Model for QSAR Data Showing Bilinear-Type Distribution

Cited by 31 publications

References 77 publications

Activity-Limiting Role of Molecular Size: Size-Dependency of Maximum Activity for P450 Inhibition as Revealed by qHTS Data

Activity-Limiting Role of Molecular Size: Size-Dependency of Maximum Activity for P450 Inhibition as Revealed by qHTS Data

Retrometabolism‐Based Drug Design and Targeting

Anti‐Inflammatory Glucocorticoids

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