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Related Concept Videos

Drug Discovery: Overview01:26

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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Gentamicin, an aminoglycoside antibiotic, is commonly administered via intermittent intravenous infusion to treat severe infections. An intermittent one-hour infusion of gentamicin, administered at eight-hour intervals, allows for precise control of plasma drug concentrations, minimizing toxicity while ensuring therapeutic efficacy. Pharmacokinetic principles govern the dynamics of plasma concentrations and can be mathematically described using specific equations.The plasma drug concentration...
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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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The empirical approach to drug therapy optimization relies on correlating pharmacological response with administered dosage. Such an approach can be costly, time-consuming, and often yields poor correlation due to variables like formulation factors and drug elimination characteristics. A more precise approach correlates response with plasma drug concentration or the amount of drug in the body, rather than dosage. This is achieved through pharmacokinetic-pharmacodynamic (PK/PD) modeling, which...
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PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure...
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Related Experiment Video

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High Content Screening in Neurodegenerative Diseases
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Central Nervous System Multiparameter Optimization Desirability: Application in Drug Discovery.

Travis T Wager1, Xinjun Hou1, Patrick R Verhoest1

  • 1Worldwide Medicinal Chemistry, Pfizer Worldwide Research and Development , 610 Main Street, Cambridge, Massachusetts 02139, United States.

ACS Chemical Neuroscience
|March 19, 2016
PubMed
Summary

The central nervous system multiparameter optimization (CNS MPO) tool enhances drug discovery by expanding design space and improving candidate quality. This approach increases the success rate of drug candidates entering clinical development.

Keywords:
AttritionCNS MPOCNS candidatesCNS drug designCNS drugsHarrington optimizationMadin−Darby canine kidneyP-glycoprotein (P-gp)central nervous system (CNS)desirability scoreefficacious drug concentration (Ceff)human liver microsome stabilityhydrogen-bond donorlipophilicitymolecular weightmost basic pKamultiparameter optimization (MPO)multivariant optimizationpassive permeabilitypolaritytopological polar surface areaunbound intrinsic clearance

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Area of Science:

  • Medicinal Chemistry
  • Drug Discovery
  • Pharmacology

Background:

  • Traditional drug design often relies on individual physicochemical parameters with strict cutoffs.
  • This can limit the exploration of novel chemical structures for central nervous system (CNS) targets.
  • A multiparameter optimization approach offers a more flexible strategy.

Purpose of the Study:

  • To evaluate the impact of the central nervous system multiparameter optimization (CNS MPO) desirability tool on drug candidate design and progression.
  • To assess the advantages of a flexible, multiparameter approach over traditional methods in CNS drug discovery.

Main Methods:

  • Analysis of a cohort of drug candidates designed using the CNS MPO tool.
  • Evaluation of physicochemical properties, ADME attributes, blood-brain barrier penetration, and safety profiles.
  • Comparison of candidate progression through preclinical and regulatory toxicology studies.

Main Results:

  • The CNS MPO tool expanded the design space for CNS drug candidates.
  • Compounds designed with CNS MPO showed improved alignment of ADME attributes, blood-brain barrier penetration, and favorable safety profiles (low ClogP, high TPSA).
  • Utilization of the tool reduced the number of compounds entering exploratory toxicity studies and increased candidate survival into First in Human studies.

Conclusions:

  • The CNS MPO algorithm effectively improves the prioritization of drug design ideas.
  • This multiparameter approach enhances the quality of drug candidates nominated for clinical development.
  • The CNS MPO tool represents a valuable advancement in CNS drug discovery.