Gαq-containing G proteins regulate B cell selection and survival and are required to prevent B cell–dependent autoimmunity

Survival of mature B cells is regulated by B cell receptor and BAFFR-dependent signals. We show that B cells from mice lacking the Gαq subunit of trimeric G proteins (Gnaq−/− mice) have an intrinsic survival advantage over normal B cells, even in the absence of BAFF. Gnaq−/− B cells develop normally in the bone marrow but inappropriately survive peripheral tolerance checkpoints, leading to the accumulation of transitional, marginal zone, and follicular B cells, many of which are autoreactive. Gnaq−/− chimeric mice rapidly develop arthritis as well as other manifestations of systemic autoimmune disease. Importantly, we demonstrate that the development of the autoreactive B cell compartment is the result of an intrinsic defect in Gnaq−/− B cells, resulting in the aberrant activation of the prosurvival factor Akt. Together, these data show for the first time that signaling through trimeric G proteins is critically important for maintaining control of peripheral B cell tolerance induction and repressing autoimmunity

Technically Extended MultiParameter Optimization (TEMPO): An Advanced Robust Scoring Scheme To Calculate Central Nervous System Druggability and Monitor Lead Optimization

At the discovery stage, it is important to understand the drug design concepts for a CNS drug compared to those for a non-CNS drug. Previously, we published on ideal CNS drug property space and defined in detail the physicochemical property distribution of CNS versus non-CNS oral drugs, the application of radar charting (a graphical representation of multiple physicochemical properties used during CNS lead optimization), and a recursive partition classification tree to differentiate between CNS- and non-CNS drugs. The objective of the present study was to further understand the differentiation of physicochemical properties between CNS and non-CNS oral drugs by the development and application of a new CNS scoring scheme: Technically Extended MultiParameter Optimization (TEMPO). In this multiparameter method, we identified eight key physicochemical properties critical for accurately assessing CNS druggability: (1) number of basic amines, (2) carbon–heteroatom (non-carbon, non-hydrogen) ratio, (3) number of aromatic rings, (4) number of chains, (5) number of rotatable bonds, (6) number of H-acceptors, (7) computed octanol/water partition coefficient (AlogP), and (8) number of nonconjugated C atoms in nonaromatic rings. Significant features of the CNS-TEMPO penalty score are the extension of the multiparameter approach to generate an accurate weight factor for each physicochemical property, the use of limits on both sides of the computed property space range during the penalty calculation, and the classification of CNS and non-CNS drug scores. CNS-TEMPO significantly outperformed CNS-MPO and the Schrödinger QikProp CNS parameter (QP_CNS) in evaluating CNS drugs and has been extensively applied in support of CNS lead optimization programs

The Effect of Pyrrolo[3,4-C]Carbazole Derivatives on Spinal Cord Chat Activity

Pyrrolo[3,4-c]carbazole derivatives were prepared as potential neurotrophic agents. The compounds were assayed for their ability to stimulate choline acetyltransferase (ChAT) activity in embryonic rat spinal cord cultures. These simplified K252a derivatives, although less potent and efficacious, have led to the identification of minimal structural requirements for K252a neurotrophic activity

Development and Scale-Up of an Optimized Route to the Pyridazin-3-one Histamine H3 Receptor Antagonist CEP-32215

The evolution of the process to prepare CEP-32215, 3-(1′-cyclobutylspiro­[4H-1,3-benzodioxine-2,4′-piperidine]-6-yl)-5,5-dimethyl-1,4-dihydropyridazine-6-one, is presented. Two routes detailing preparation of supplies for biological screening are discussed along with the optimized fit-for-purpose process used to prepare several hundred grams for preclinical testing. Details on the development of the formation of the key spiroketal moiety are presented along with the discovery of a novel Suzuki coupling approach for synthesis of the backbone of the molecule

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

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 Ų (25–60 Ų), (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 ų, (vi) solvent accessible surface area of 460–580 Ų, 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

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

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 Ų (25–60 Ų), (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 ų, (vi) solvent accessible surface area of 460–580 Ų, 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

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

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 Ų (25–60 Ų), (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 ų, (vi) solvent accessible surface area of 460–580 Ų, 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