Macrocycles, the edge of drug‐likeness chemical space or Goldilocks zone?

Selwood, David L.

doi:10.1111/cbdd.12922

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…Furthermore, this variation in ring sizes between known inhibitors made us wonder whether the current ring size is already optimal for each macrocycle. 27,28 However, no such SAR explorations have been reported. To address how ring size may affect the biological activity of ipomoeassin F, one of the most potent Sec61 inhibitors identified to date, we designed two new analogues (1 and 2, Figure 1) with an 18-membered and 22-membered ring, respectively.…”

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confidence: 99%

“…Interestingly, excluding the synthetic compound eeyarestatin I, all the other Sec61α inhibitors belong to one of the distinct classes of macrocyclic natural products with different ring sizes: cotransins (21-membered), , apratoxins (25-membered), , mycolactone (12-membered), − decatransin (30-membered), coibamide A (22-membered), , and ipomoeassins (20-membered). , Thus, it appears that a macrocyclic ring is a preferred structural feature among Sec61α-targeting inhibitors. Furthermore, this variation in ring sizes between known inhibitors made us wonder whether the current ring size is already optimal for each macrocycle. , However, no such SAR explorations have been reported. To address how ring size may affect the biological activity of ipomoeassin F, one of the most potent Sec61 inhibitors identified to date, we designed two new analogues ( 1 and 2 , Figure ) with an 18-membered and 22-membered ring, respectively.…”

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confidence: 99%

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Ring Expansion Leads to a More Potent Analogue of Ipomoeassin F

Zong

Duah

et al. 2020

J. Org. Chem.

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Two new ring-size-varying analogues (2 and 3) of ipomoeassin F were synthesized and evaluated. Improved cytotoxicity (IC50: from 1.8 nM) and in vitro protein translocation inhibition (IC50: 35 nM) derived from ring expansion imply that the binding pocket of Sec61α (isoform 1) can accommodate further structural modifications, likely in the fatty acid portion. Streamlined preparation of the key diol intermediate 5 enabled gram-scale production, allowing us to establish that ipomoeassin F is biologically active in vivo (MTD: ∼3 mg/kg).

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confidence: 99%

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confidence: 99%

Ring Expansion Leads to a More Potent Analogue of Ipomoeassin F

Zong

Duah

et al. 2020

J. Org. Chem.

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“…Inspection of available data suggests that most compounds appear to abide within the limit of HBD < 5 as illustrated in Figure 4. Despite this evidence, there has been a recent upsurge in the number of drugs approved for therapeutic use that tend to violate HBD and pertinent Ro5 criteria [69][70][71][72]. In view of these developments, suggestions to expand such limits have been considered and a modified version designated as beyond Ro5 (bRo5) proposed [69].…”

Section: Criteria For Selecting Prospective Lead Compoundsmentioning

confidence: 99%

Forces Driving a Magic Bullet to Its Target: Revisiting the Role of Thermodynamics in Drug Design, Development, and Optimization

Minetti

Remeta

2022

Life

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Drug discovery strategies have advanced significantly towards prioritizing target selectivity to achieve the longstanding goal of identifying “magic bullets” amongst thousands of chemical molecules screened for therapeutic efficacy. A myriad of emerging and existing health threats, including the SARS-CoV-2 pandemic, alarming increase in bacterial resistance, and potentially fatal chronic ailments, such as cancer, cardiovascular disease, and neurodegeneration, have incentivized the discovery of novel therapeutics in treatment regimens. The design, development, and optimization of lead compounds represent an arduous and time-consuming process that necessitates the assessment of specific criteria and metrics derived via multidisciplinary approaches incorporating functional, structural, and energetic properties. The present review focuses on specific methodologies and technologies aimed at advancing drug development with particular emphasis on the role of thermodynamics in elucidating the underlying forces governing ligand–target interaction selectivity and specificity. In the pursuit of novel therapeutics, isothermal titration calorimetry (ITC) has been utilized extensively over the past two decades to bolster drug discovery efforts, yielding information-rich thermodynamic binding signatures. A wealth of studies recognizes the need for mining thermodynamic databases to critically examine and evaluate prospective drug candidates on the basis of available metrics. The ultimate power and utility of thermodynamics within drug discovery strategies reside in the characterization and comparison of intrinsic binding signatures that facilitate the elucidation of structural–energetic correlations which assist in lead compound identification and optimization to improve overall therapeutic efficacy.

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“…Macrocycles are scaffolds with a ring containing at least 12 atoms (Mallinson and Collins, 2012). Macrocycles also contain many desirable properties such as, less rigidity and flexibility, high binding capabilities, having affinity to anions and cations, high bio-availability, and the ability to target protein-protein interactions (PPI) (Choi and Hamilton, 2003;Dougherty et al, 2017;Ermert, 2017;Selwood, 2017).…”

Section: Introductionmentioning

confidence: 99%

DeepMalaria: Artificial Intelligence Driven Discovery of Potent Antiplasmodials

et al. 2020

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Antimalarial drugs are becoming less effective due to the emergence of drug resistance. Resistance has been reported for all available malaria drugs, including artemisinin, thus creating a perpetual need for alternative drug candidates. The traditional drug discovery approach of high throughput screening (HTS) of large compound libraries for identification of new drug leads is time-consuming and resource intensive. While virtual in silico screening is a solution to this problem, however, the generalization of the models is not ideal. Artificial intelligence (AI), utilizing either structure-based or ligand-based approaches, has demonstrated highly accurate performances in the field of chemical property prediction. Leveraging the existing data, AI would be a suitable alternative to blind-search HTS or fingerprint-based virtual screening. The AI model would learn patterns within the data and help to search for hit compounds efficiently. In this work, we introduce DeepMalaria, a deeplearning based process capable of predicting the anti-Plasmodium falciparum inhibitory properties of compounds using their SMILES. A graph-based model is trained on 13,446 publicly available antiplasmodial hit compounds from GlaxoSmithKline (GSK) dataset that are currently being used to find novel drug candidates for malaria. We validated this model by predicting hit compounds from a macrocyclic compound library and already approved drugs that are used for repurposing. We have chosen macrocyclic compounds as these ligandbinding structures are underexplored in malaria drug discovery. The in silico pipeline for this process also consists of additional validation of an in-house independent dataset consisting mostly of natural product compounds. Transfer learning from a large dataset was leveraged to improve the performance of the deep learning model. To validate the DeepMalaria generated hits, we used a commonly used SYBR Green I fluorescence assay based phenotypic screening. DeepMalaria was able to detect all the compounds with nanomolar activity and 87.5% of the compounds with greater than 50% inhibition. Further experiments to reveal the compounds' mechanism of action have shown that not only does one of the hit compounds, DC-9237, inhibits all asexual stages of Plasmodium falciparum, but is a fast-acting compound which makes it a strong candidate for further optimization.

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Macrocycles, the edge of drug‐likeness chemical space or Goldilocks zone?

Cited by 5 publications

References 22 publications

Ring Expansion Leads to a More Potent Analogue of Ipomoeassin F

Ring Expansion Leads to a More Potent Analogue of Ipomoeassin F

Forces Driving a Magic Bullet to Its Target: Revisiting the Role of Thermodynamics in Drug Design, Development, and Optimization

DeepMalaria: Artificial Intelligence Driven Discovery of Potent Antiplasmodials

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