Targeted and
Specialty Libraries

CNS
Diverse, High-Quality Compounds for CNS Disease Research

ChemBridge has created a CNS-focused compound selection, the CNS-MPO Library, to support CNS drug discovery programs. Compounds from the library are predicted to have a higher probability of crossing the blood-brain barrier (BBB) based on a multiparameter optimization (MPO) approach. The CNS-MPO Library represents a subset of compounds from ChemBridge’s stock of more than 1.3 million lead-like and drug-like small molecule screening compounds representing a wide range of different chemotypes.

View the CNS-MPO Library product sheet

Highlights
  • High quality, PAINS-free, small molecule compounds for CNS drug discovery
  • CNS MPO analysis applied to identify compounds with high probability of blood-brain barrier (BBB) penetration and improved clearance and safety profiles
  • Custom select from more than 450,000 structures to meet your specific requirements
  • Up to 50,000 CNS-MPO Library compounds available in pre-plated format
  • Includes structural analogs for SAR studies
CNS Multiparameter Optimization (MPO) Score

A fundamental challenge for the design of CNS penetrant drugs is the need to cross the BBB. Physiochemical parameters for BBB permeable compounds form a smaller subset within the property space of oral drugs. To best define the physicochemical properties for CNS library design ChemBridge selected a weighted scoring approach described by Wager et al.1,2 The CNS MPO score is now a well-recognized algorithm in the CNS focused medicinal chemistry community. The algorithm uses a weighted scoring function assessing 6 key physicochemical properties (clogP, clogD, MW, TPSA, HBD, and pKa) for BBB penetration, CYP mediated metabolism and inhibition of dofetilide binding. The CNS MPO score is between 0 and 6.0 with scores ≥ 4.0 widely used as a cut-off to select compounds for hit finding in CNS therapeutic area drug discovery programs. A recent article assessing 616 compounds with measured unbound concentrations in the brain confirmed increasing CNS MPO score correlates with increased unbound concentration in the brain.3

Selection

The following approach was used to select compounds for inclusion in the CNS-MPO Library:

  • Filter to remove compounds with undesirable structural features including PAINS filters
  • Remove compounds with carboxylic acid groups due to low probability of BBB permeability4
  • MW range of 250 to 450 inclusive to allow for hit-finding or lead-like biased selections
  • CNS MPO score ≥ 4.0
  • Remove compounds with TPSA ≥ 100Å to eliminate high MPO score compounds with less desirable TPSA
  • Limit clogD range to 0 to 5.0 inclusive to eliminate high MPO score compounds with less desirable clogD values

The CNS-MPO Library selection can be further refined by using structural diversity analysis, by focusing on a hit-finding subset (MW range 300 to 450), or by focusing on a lead-like subset (MW range 250 to 400; TPSA ≤ 90Å; option to re-score using CNS Lead MPO5). Up to 50,000 compounds are also available in pre-plated format.

Format
  • Download structures and custom select from more than 450,000 CNS-MPO Library compounds
  • Up to 50,000 CNS-MPO Library compounds available in pre-plated format
  • Compounds can be provided in 96-well or 384-well format
  • Available in 96-well or 384-well format including acoustic compatible plates
  • Amounts as low as 0.25 micromole (25ul of 10mM DMSO solution) available
  • Compounds are available as DMSO solutions or dry in micromole or mg amounts

For more information or a file of compound structures, please contact ChemBridge Sales

References
  1. Wager TT et al. Moving beyond rules: The development of a central nervous system multiparameter optimization (CNS MPO) approach to enable alignment of druglike properties. ACS Chem. Neurosci. 2010 1, 435-449
  2. Wager TT et al. Central nervous system multiparameter optimization desirability: Application in drug discovery. ACS Chem. Neurosci. 2016 7, 767-7
  3. Rankovic Z CNS drug design: balancing physicochemical properties for optimal brain exposure. J. Med. Chem. 2015 58 (6), 2584–2608
  4. Ghose AK et al. Knowledge-based, central nervous system (CNS) lead selection and lead optimization for CNS drug discovery. ACS Chem. Neurosci. 2012 3, 50−68
  5. Mayol-Llinàs J et al. Assessing molecular scaffolds for CNS drug discovery. Drug Discov. Today. 2017 7, 965-969