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Xinjun Hou

Bio: Xinjun Hou is an academic researcher from Pfizer. The author has contributed to research in topics: Cathepsin D & Sulfamide. The author has an hindex of 24, co-authored 37 publications receiving 2329 citations.

Papers
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Journal ArticleDOI
TL;DR: Based on six physicochemical properties commonly used by medicinal chemists, the CNS MPO function may be used prospectively at the design stage to accelerate the identification of compounds with increased probability of success.
Abstract: The interplay among commonly used physicochemical properties in drug design was examined and utilized to create a prospective design tool focused on the alignment of key druglike attributes. Using a set of six physicochemical parameters ((a) lipophilicity, calculated partition coefficient (ClogP); (b) calculated distribution coefficient at pH = 7.4 (ClogD); (c) molecular weight (MW); (d) topological polar surface area (TPSA); (e) number of hydrogen bond donors (HBD); (f) most basic center (pKa)), a druglikeness central nervous system multiparameter optimization (CNS MPO) algorithm was built and applied to a set of marketed CNS drugs (N = 119) and Pfizer CNS candidates (N = 108), as well as to a large diversity set of Pfizer proprietary compounds (N = 11 303). The novel CNS MPO algorithm showed that 74% of marketed CNS drugs displayed a high CNS MPO score (MPO desirability score ≥ 4, using a scale of 0−6), in comparison to 60% of the Pfizer CNS candidates. This analysis suggests that this algorithm could p...

713 citations

Journal ArticleDOI
TL;DR: A thorough analysis of properties for 119 marketed CNS drugs and a set of 108 Pfizer CNS candidates focused on understanding the relationships between physicochemical properties, in vitro ADME attributes, primary pharmacology binding efficiencies, and in vitro safety data.
Abstract: As part of our effort to increase survival of drug candidates and to move our medicinal chemistry design to higher probability space for success in the Neuroscience therapeutic area, we embarked on a detailed study of the property space for a collection of central nervous system (CNS) molecules. We carried out a thorough analysis of properties for 119 marketed CNS drugs and a set of 108 Pfizer CNS candidates. In particular, we focused on understanding the relationships between physicochemical properties, in vitro ADME (absorption, distribution, metabolism, and elimination) attributes, primary pharmacology binding efficiencies, and in vitro safety data for these two sets of compounds. This scholarship provides guidance for the design of CNS molecules in a property space with increased probability of success and may lead to the identification of druglike candidates with favorable safety profiles that can successfully test hypotheses in the clinic.

368 citations

Journal ArticleDOI
TL;DR: The CNS MPO tool has helped to increase the percentage of compounds nominated for clinical development that exhibit alignment of ADME attributes, cross the blood-brain barrier, and reside in lower-risk safety space (low ClogP and high TPSA).
Abstract: Significant progress has been made in prospectively designing molecules using the central nervous system multiparameter optimization (CNS MPO) desirability tool, as evidenced by the analysis reported herein of a second wave of drug candidates that originated after the development and implementation of this tool. This simple-to-use design algorithm has expanded design space for CNS candidates and has further demonstrated the advantages of utilizing a flexible, multiparameter approach in drug discovery rather than individual parameters and hard cutoffs of physicochemical properties. The CNS MPO tool has helped to increase the percentage of compounds nominated for clinical development that exhibit alignment of ADME attributes, cross the blood–brain barrier, and reside in lower-risk safety space (low ClogP and high TPSA). The use of this tool has played a role in reducing the number of compounds submitted to exploratory toxicity studies and increasing the survival of our drug candidates through regulatory tox...

287 citations

Journal ArticleDOI
TL;DR: This PDE10A inhibitor is the first reported clinical entry for this mechanism in the treatment of schizophrenia and the discovery of 2-[4-(1-methyl-4-pyridin- 4-yl-1H-pyrazol-3-yl)-phenoxymethyl]-quinoline (PF-2545920).
Abstract: By utilizing structure-based drug design (SBDD) knowledge, a novel class of phosphodiesterase (PDE) 10A inhibitors was identified. The structure-based drug design efforts identified a unique "selectivity pocket" for PDE10A inhibitors, and interactions within this pocket allowed the design of highly selective and potent PDE10A inhibitors. Further optimization of brain penetration and drug-like properties led to the discovery of 2-[4-(1-methyl-4-pyridin-4-yl-1H-pyrazol-3-yl)-phenoxymethyl]-quinoline (PF-2545920). This PDE10A inhibitor is the first reported clinical entry for this mechanism in the treatment of schizophrenia.

201 citations

Journal ArticleDOI
TL;DR: A database consisting of 62 PET ligands that have successfully reached the clinic and 15 radioligands that failed in late-stage development as negative controls was built, which identified a set of preferred parameters for physicochemical properties, brain permeability, and nonspecific binding.
Abstract: To accelerate the discovery of novel small molecule central nervous system (CNS) positron emission tomography (PET) ligands, we aimed to define a property space that would facilitate ligand design and prioritization, thereby providing a higher probability of success for novel PET ligand development. Toward this end, we built a database consisting of 62 PET ligands that have successfully reached the clinic and 15 radioligands that failed in late-stage development as negative controls. A systematic analysis of these ligands identified a set of preferred parameters for physicochemical properties, brain permeability, and nonspecific binding (NSB). These preferred parameters have subsequently been applied to several programs and have led to the successful development of novel PET ligands with reduced resources and timelines. This strategy is illustrated here by the discovery of the novel phosphodiesterase 2A (PDE2A) PET ligand 4-(3-[18F]fluoroazetidin-1-yl)-7-methyl-5-{1-methyl-5-[4-(trifluoromethyl)phenyl]-1H...

153 citations


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Book
17 May 2013
TL;DR: This research presents a novel and scalable approach called “Smartfitting” that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of designing and implementing statistical models for regression models.
Abstract: General Strategies.- Regression Models.- Classification Models.- Other Considerations.- Appendix.- References.- Indices.

3,672 citations

Journal ArticleDOI
TL;DR: In this Perspective, applications of fluorine in the construction of bioisosteric elements designed to enhance the in vitro and in vivo properties of a molecule are summarized.
Abstract: The electronic properties and relatively small size of fluorine endow it with considerable versatility as a bioisostere and it has found application as a substitute for lone pairs of electrons, the hydrogen atom, and the methyl group while also acting as a functional mimetic of the carbonyl, carbinol, and nitrile moieties. In this context, fluorine substitution can influence the potency, conformation, metabolism, membrane permeability, and P-gp recognition of a molecule and temper inhibition of the hERG channel by basic amines. However, as a consequence of the unique properties of fluorine, it features prominently in the design of higher order structural metaphors that are more esoteric in their conception and which reflect a more sophisticated molecular construction that broadens biological mimesis. In this Perspective, applications of fluorine in the construction of bioisosteric elements designed to enhance the in vitro and in vivo properties of a molecule are summarized.

1,199 citations

Journal ArticleDOI
TL;DR: This article compile and review the literature on molecular interactions as it pertains to medicinal chemistry through a combination of careful statistical analysis of the large body of publicly available X-ray structure data and experimental and theoretical studies of specific model systems.
Abstract: Molecular recognition in biological systems relies on the existence of specific attractive interactions between two partner molecules. Structure-based drug design seeks to identify and optimize such interactions between ligands and their host molecules, typically proteins, given their three-dimensional structures. This optimization process requires knowledge about interaction geometries and approximate affinity contributions of attractive interactions that can be gleaned from crystal structure and associated affinity data. Here we compile and review the literature on molecular interactions as it pertains to medicinal chemistry through a combination of careful statistical analysis of the large body of publicly available X-ray structure data and experimental and theoretical studies of specific model systems. We attempt to extract key messages of practical value and complement references with our own searches of the CSDa,(1) and PDB databases.(2) The focus is on direct contacts between ligand and protein functional groups, and we restrict ourselves to those interactions that are most frequent in medicinal chemistry applications. Examples from supramolecular chemistry and quantum mechanical or molecular mechanics calculations are cited where they illustrate a specific point. The application of automated design processes is not covered nor is design of physicochemical properties of molecules such as permeability or solubility. Throughout this article, we wish to raise the readers’ awareness that formulating rules for molecular interactions is only possible within certain boundaries. The combination of 3D structure analysis with binding free energies does not yield a complete understanding of the energetic contributions of individual interactions. The reasons for this are widely known but not always fully appreciated. While it would be desirable to associate observed interactions with energy terms, we have to accept that molecular interactions behave in a highly nonadditive fashion.3,4 The same interaction may be worth different amounts of free energy in different contexts, and it is very hard to find an objective frame of reference for an interaction, since any change of a molecular structure will have multiple effects. One can easily fall victim to confirmation bias, focusing on what one has observed before and building causal relationships on too few observations. In reality, the multiplicity of interactions present in a single protein−ligand complex is a compromise of attractive and repulsive interactions that is almost impossible to deconvolute. By focusing on observed interactions, one neglects a large part of the thermodynamic cycle represented by a binding free energy: solvation processes, long-range interactions, conformational changes. Also, crystal structure coordinates give misleadingly static views of interactions. In reality a macromolecular complex is not characterized by a single structure but by an ensemble of structures. Changes in the degrees of freedom of both partners during the binding event have a large impact on binding free energy. The text is organized in the following way. The first section treats general aspects of molecular design: enthalpic and entropic components of binding free energy, flexibility, solvation, and the treatment of individual water molecules, as well as repulsive interactions. The second half of the article is devoted to specific types of interactions, beginning with hydrogen bonds, moving on to weaker polar interactions, and ending with lipophilic interactions between aliphatic and aromatic systems. We show many examples of structure−activity relationships; these are meant as helpful illustrations but individually can never confirm a rule.

1,162 citations

Journal ArticleDOI
TL;DR: The utility of QED is extended by applying it to the problem of molecular target druggability assessment by prioritizing a large set of published bioactive compounds and may also capture the abstract notion of aesthetics in medicinal chemistry.
Abstract: Drug-likeness is a key consideration when selecting compounds during the early stages of drug discovery. However, evaluation of drug-likeness in absolute terms does not reflect adequately the whole spectrum of compound quality. More worryingly, widely used rules may inadvertently foster undesirable molecular property inflation as they permit the encroachment of rule-compliant compounds towards their boundaries. We propose a measure of drug-likeness based on the concept of desirability called the quantitative estimate of drug-likeness (QED). The empirical rationale of QED reflects the underlying distribution of molecular properties. QED is intuitive, transparent, straightforward to implement in many practical settings and allows compounds to be ranked by their relative merit. We extended the utility of QED by applying it to the problem of molecular target druggability assessment by prioritizing a large set of published bioactive compounds. The measure may also capture the abstract notion of aesthetics in medicinal chemistry.

1,161 citations

Journal ArticleDOI
TL;DR: This paper presents a meta-review of the literature on Vinyl Sulfones, Michael Acceptors, and Heterocyclic Inhibitors dating back to the 1970s, which revealed a wide diversity of opinions about the properties of these substances and their role in the human immune system.
Abstract: F. Vinyl Sulfones and Other Michael Acceptors 4683 G. Azodicarboxamides 4695 IV. Acylating Agents 4695 A. Aza-peptides 4695 B. Carbamates 4699 C. Peptidyl Acyl Hydroxamates 4700 D. â-Lactams and Related Inhibitors 4704 E. Heterocyclic Inhibitors 4714 1. Isocoumarins 4715 2. Benzoxazinones 4722 3. Saccharins 4725 4. Miscellaneous Heterocyclic Inhibitors 4728 V. Phosphonylation Agents 4728 A. Peptide Phosphonates 4728 B. Phosphonyl Fluorides 4734 VI. Sulfonylating Agents 4735 A. Sulfonyl Fluorides 4735 VII. Miscellaneous Inhibitors 4736 VIII. Summary and Perspectives 4737 IX. Acknowledgments 4740 X. Note Added in Proof 4740 XI. References 4740

961 citations