Knowledge-Based, Central Nervous System (CNS) Lead Selection and Lead Optimization for CNS Drug Discovery

By A. Ghose, T. Herbertz, R. Hudkins, B. Dorsey, J. Mallamo. 2011

393 citations recorded by the source corpus.

The central nervous system (CNS) is the major area that is affected by aging. Alzheimer’s disease (AD), Parkinson’s disease (PD), brain cancer, and stroke are the CNS diseases that will cost trillions of dollars for their treatment. Achievement of appropriate blood–brain barrier (BBB) penetration is often considered a significant hurdle in the CNS drug discovery process. On the other hand, BBB penetration may be a liability for many of the non-CNS drug targets, and a clear understanding of the physicochemical and structural differences between CNS and non-CNS drugs may assist both research areas. Because of the numerous and challenging issues in CNS drug discovery and the low success rates, pharmaceutical companies are beginning to deprioritize their drug discovery efforts in the CNS arena. Prompted by these challenges and to aid in the design of high-quality, efficacious CNS compounds, we analyzed the physicochemical property and the chemical structural profiles of 317 CNS and 626 non-CNS oral drugs. The conclusions derived provide an ideal property profile for lead selection and the property modification strategy during the lead optimization process. A list of substructural units that may be useful for CNS drug design was also provided here. A classification tree was also developed to differentiate between CNS drugs and non-CNS oral drugs. The combined analysis provided the following guidelines for designing high-quality CNS drugs: (i) topological molecular polar surface area of <76 Å2 (25–60 Å2), (ii) at least one (one or two, including one aliphatic amine) nitrogen, (iii) fewer than seven (two to four) linear chains outside of rings, (iv) fewer than three (zero or one) polar hydrogen atoms, (v) volume of 740–970 Å3, (vi) solvent accessible surface area of 460–580 Å2, and (vii) positive QikProp parameter CNS. The ranges within parentheses may be used during lead optimization. One violation to this proposed profile may be acceptable. The chemoinformatics approaches for graphically analyzing multiple properties efficiently are presented.

Subfields: Computational Theory and Mathematics, Oncology, Pharmacology, Computational Chemistry & Drug Discovery

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In Computational Theory and Mathematics

1. C. K. Surratt
2. Jeffry D. Madura
3. Michael J. Wasko
4. Kendy A. Pellegrene

In Pharmacology

1. C. K. Surratt
2. Jeffry D. Madura

In Computational Chemistry & Drug Discovery

1. C. K. Surratt
2. Jeffry D. Madura
3. Michael J. Wasko
4. Kendy A. Pellegrene

Citations in the corpus

• A Role for Fragment-Based Drug Design in Developing Novel Lead Compounds for Central Nervous System Targets (2015, background): “Central nervous system (CNS) disorders are logical foci for such new strategies; the increasingly geriatric population is more susceptible to Alzheimer’s disease, Parkinson’s disease, and ischemic stroke (4).”
• Current Challenges and Opportunities in Treating Glioblastoma (2018, background): “Generally, CNS drugs are smaller and more lipophilic at A SPE T Journals on Septem ber 18, 2023 pharm rev.aspetjournals.org D ow nloaded from than oral non-CNS drugs and have fewer hydrogen bond donors and a lower topological polar surface area (Ghose et al., 2012).”