Using Descriptor Cutoffs and Multiparameter Optimization for Preparation of CNS Targeted Libraries

Expert-driven In Silico Drug Discovery Solutions
1 September 2020
Svitlana Kondovych
Senior Researcher

Disorders of the central nervous system (CNS) have been constantly puzzling the global health research community due to their plurality, diversity, and, above all, intractability. The human brain is an extremely well-protected organ, which is undoubtedly beneficial to us; however, this complex security system turns up to be a double-edged sword. It is hard to damage the brain. It is not any easier to cure it.

The most significant factor that constrains the discovery of new CNS drugs is the limited blood-brain barrier (BBB) penetrability. More than 98 % of small-molecule drugs do not cross BBB, whereas larger molecules essentially never succeed [1]. Thus, increasing BBB-targeting efficiency is one of the key issues in the design of novel CNS-active medications. Recent advances in CNS drug research rest upon medicinal chemistry [2-4] and nanotechnology-based approaches [3] to go through the BBB.

To optimize the study of potentially CNS-active compounds, researchers attempt to define the physicochemical characteristics of the successful CNS drugs and derive the favorable selection rules. Numerous reviews and statistical studies (see, e.g., [4] and references therein) aim to systematize available BBB permeation data, extract correlations, and suggest strategies on choosing the working drug.

The main parameters that determine a drug permeability include the molecular weight (MW) of a compound, its topological polar surface area (TPSA), charge, and lipophilicity. The latter is usually characterized by the calculated octanol-water partition coefficient (ClogP), as well as by its value at physiological pH = 7.4 (ClogD). Overall, CNS-targeted drugs appear to have high lipophilicity and low flexibility, they have fewer hydrogen-bond donors (HbD < 3) and acceptors (HbAc < 7), reduced molecular weight (150 – 400 Da), fewer formal charges (particularly, negative charges) and lower polar surface area (TPSA ≤ 65 Å2). The exact cut-off values for each of these parameters make up the subject of continuous search, elaboration, and discussion in the scientific community [1,2,4-18].

In most cases, to explain and predict CNS-related pharmacokinetic properties, one needs to solve a complex multi-parameter puzzle, taking into account not only values for individual physicochemical parameters but also considering their interplay. A review of the multivariate screening methods and their application is given in [4,19]. Among the most widely employed techniques are the CNS multiparameter optimization (MPO) [17] and the “Golden Triangle” rule [18,20].

Fig. 1. Multiparameter optimization approach comprises six physicochemical parameters

Fig. 2. The “Golden Triangle” rule utilizes the correlation between MW and lipophilicity

The former method implies the simultaneous estimation of the set of six parameters, namely: MW, TPSA, ClogP, ClogD, HbD, and the acid-base dissociation constant, pKa (Fig. 1). The algorithm returns the MPO desirability score, a number from 0 to 6, which may serve as a reference point for CNS drug design ideas. As a rule, the threshold value of MPO score ≥ 4 identifies the potential CNS-active compounds [17].

The latter approach – the “Golden Triangle” rule – involves joint estimation of molecular weight and lipophilicity ClogD of potential drugs. In fact, the limit of ClogD depends on MW [18]: greater lipophilicity corresponds to lower MWs, resulting in a triangle shape of the graph (Fig. 2) for acceptable drug-like compounds [20]. Considering this correlation may provide better predictions than treating the parameters separately.

Life Chemicals follows the most up-to-date criteria to offer its collection of compounds with desired CNS properties. The Life Chemicals CNS Screening Library comprises over 9,900 small organic molecules, carefully singled out from the database using the optimal threshold values for the main physicochemical parameters. Fig. 3 illustrates the resulting distributions and the calculated MPO desirability score.

To prevent the failure in BBB transport we additionally optimize the number of S, Cl and N atoms in the CNS-targeted compounds, ensure the absence of quaternary ammonium groups and allow not more than one carboxylic acid group [1]. All the compounds are subject to PAINS and toxicophore filters.

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 Distribution of the main physicochemical parameters and calculated MPO score  in the Life Chemicals CNS Library

Fig. 3. Distribution of the main physicochemical parameters and calculated MPO score 
in the Life Chemicals CNS Library



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1 September 2020, 12:11 Svitlana Kondovych Computational Chemistry

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