Pharma Algorithms. www.ap-algorithms.com. Classification Analysis (C-SAR) in Predicting Pgp Substrate Specificity. R. Didziapetris, P. Japertas, A. Petrauskas. Pharma Algorithms. Classification SAR. ►. Based on recursive partitioning Groups compounds into classes
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Classification Analysis (C-SAR) in
Predicting Pgp Substrate Specificity
R. Didziapetris, P. Japertas, A. Petrauskas
Based on recursive partitioning
Groups compounds into classes
Aims at differentiating biol. mechanisms
A multistep procedure
At each step, finds the best descriptor with the best cut-off value
Single-click conversion into decision tree
MV > 300
Acid pKa > 5
850 compounds compiled: 322 substrates + 528 non-substrates
“Two-class” model used: 1 – substrates, 0 – non-substrates
P-gp substrate specificity analyzed, not inhibition, and not induction
Tamoxifen - 0
More data required
No acid group with pKa < 5
Ertl’s tPSA > 72 A2, log P > 0.58, Abraham's beta >1.8, alpha = 0.5 - 3.3
MV = 534 to 674 cm3/mol(MW = c.a. 700 to 900)
True: 50False: 3
Two types of “biophores”:
Depend on biological class of compounds
Do not depend on biological class of compounds
Lead to the “knowledge- based” filters
Lead to the “statistical induction” algorithms
Similar skeletons were also identified for anthracyclines, talinolol, quinidine and etoposide groups.
The good statistical significance + interchangeability of biophores
Seelig-type non-specific fragments - many alternative biophores produce equally good results
“Knowledge” approach is preferable, but more data is needed.
“Statistical” approach produces higher % of false predictions.
Two approaches can be used together.