Modern Lead Generation Strategies

Holenz, Jörg; Brown, Dean G.

doi:10.1002/9783527677047.ch02

Cited by 10 publications

(9 citation statements)

References 45 publications

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“…One of the most important decisions a drug discovery team can make is the choice of lead generation strategy to identify chemical starting points. − Traditional lead generation strategies have been random high throughput screening (HTS), fragment-based lead generation (FBLG), structure-based drug design (SBDD), utilization of known literature such as fast-follower or knowledge-based programs, and more recently DNA-encoded library screening (DEL) . The choice of which strategy to employ is dependent on multiple factors such as the technical demands for each approach, overall costs, and access to appropriate screening libraries or chemical starting points for each technology.…”

Section: Introductionmentioning

confidence: 99%

Where Do Recent Small Molecule Clinical Development Candidates Come From?

2018

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An analysis of 66 published clinical candidates from Journal of Medicinal Chemistry has been conducted to shed light on which lead generation strategies are most frequently employed in identifying drug candidates. The most frequent lead generation strategy (producing a drug candidate) was based on starting points derived from previously known compounds (43%) followed by random high throughput screening (29%). The remainder of approaches included focused screening, structure-based drug design (SBDD), fragment-based lead generation (FBLG), and DNA-encoded library screening (DEL). An analysis of physicochemical properties on the hit-to-clinical pairs shows an average increase in molecular weight (ΔMW = +85) but no change in lipophilicity (ΔclogP = -0.2), although exceptions are noted. The majority (>50%) of clinical candidates were found to be structurally very different from their starting point and were more complex. Finally, several reports of noncovalent scaffolds modified by a covalent warhead using SBDD approaches are discussed.

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Section: Introductionmentioning

confidence: 99%

Where Do Recent Small Molecule Clinical Development Candidates Come From?

2018

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“…Moreover, the iterative learning process of medicinal chemistry defining structure–activity relationships (SAR) is composed by computational design, compound synthesis, biological assays, and data collection whose analysis drives the next learning cycle ( Figure 1 B). 13 Typically, cycle stages are compartmentalized, compounding delays from hypothesis to results, slow explorations, and a limited number of compounds for clinical trials. Strategies aimed at integrating the diverse disciplines and facilitating operations within the single compartment are therefore highly desirable.…”

Section: The Medicinal Chemistry (R)evolution: Drawbacks and Technolomentioning

confidence: 99%

The Medicinal Chemistry in the Era of Machines and Automation: Recent Advances in Continuous Flow Technology

et al. 2020

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Medicinal chemistry plays a fundamental and underlying role in chemical biology, pharmacology, and medicine to discover safe and efficacious drugs. Small molecule medicinal chemistry relies on iterative learning cycles composed of compound design, synthesis, testing, and data analysis to provide new chemical probes and lead compounds for novel and druggable targets. Using traditional approaches, the time from hypothesis to obtaining the results can be protracted, thus limiting the number of compounds that can be advanced into clinical studies. This challenge can be tackled with the recourse of enabling technologies that are showing great potential in improving the drug discovery process. In this Perspective, we highlight recent developments toward innovative medicinal chemistry strategies based on continuous flow systems coupled with automation and bioassays. After a discussion of the aims and concepts, we describe equipment and representative examples of automated flow systems and end-to-end prototypes realized to expedite medicinal chemistry discovery cycles.

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“…In some respects, the “me-too” compound may present a different pharmacokinetics profile relative to the parent drug, but uses the same molecular mechanism as the parent drug and is used for the same therapeutic purpose as the parent drug [ 90 ]. Besides “me-too” compounds, the “me-better” compounds (also called best-in-class) [ 91 ] represent leader compounds, with improved activity, selectivity and potency over original compounds [ 90 ].…”

Section: Brief Overview Of Bioinformatics and Cheminformatics Tools Amentioning

confidence: 99%

Potential Therapeutic Approaches to Alzheimer’s Disease By Bioinformatics, Cheminformatics And Predicted Adme-Tox Tools

Avram

Mernea

Limban

et al. 2020

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Background: Alzheimer’s disease (AD) is considered a severe, irreversible and progressive neurodegenerative disorder. Currently, the pharmacological management of AD is based on a few clinically approved acethylcholinesterase (AChE) and N-methyl-D-aspartate (NMDA) receptor ligands, with unclear molecular mechanisms and severe side effects. Methods: Here, we reviewed the most recent bioinformatics, cheminformatics (SAR, drug design, molecular docking, friendly databases, ADME-Tox) and experimental data on relevant structure-biological activity relationships and molecular mechanisms of some natural and synthetic compounds with possible anti-AD effects (inhibitors of AChE, NMDA receptors, beta-secretase, amyloid beta (Aβ), redox metals) or acting on multiple AD targets at once. We considered: (i) in silico supported by experimental studies regarding the pharmacological potential of natural compounds as resveratrol, natural alkaloids, flavonoids isolated from various plants and donepezil, galantamine, rivastagmine and memantine derivatives, (ii) the most important pharmacokinetic descriptors of natural compounds in comparison with donepezil, memantine and galantamine. Results: In silico and experimental methods applied to synthetic compounds led to the identification of new AChE inhibitors, NMDA antagonists, multipotent hybrids targeting different AD processes and metal-organic compounds acting as Aβ inhibitors. Natural compounds appear as multipotent agents, acting on several AD pathways: cholinesterases, NMDA receptors, secretases or Aβ, but their efficiency in vivo and their correct dosage is to be determined. Conclusion: Bioinformatics, cheminformatics and ADME-Tox methods can be very helpful in the quest for an effective anti-AD treatment, allowing the identification of novel drugs, enhancing the druggability of molecular targets and providing a deeper understanding of AD pathological mechanisms.

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Modern Lead Generation Strategies

Cited by 10 publications

References 45 publications

Where Do Recent Small Molecule Clinical Development Candidates Come From?

Where Do Recent Small Molecule Clinical Development Candidates Come From?

The Medicinal Chemistry in the Era of Machines and Automation: Recent Advances in Continuous Flow Technology

Potential Therapeutic Approaches to Alzheimer’s Disease By Bioinformatics, Cheminformatics And Predicted Adme-Tox Tools

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