A molecular informatics view on best practice in multi-parameter compound optimization

Lusher, Scott J.; McGuire, Ross; Azevedo, Rita; Boiten, Jan-Willem; Schaik, René C. van; Vlieg, Jacob de

doi:10.1016/j.drudis.2011.05.005

Cited by 19 publications

(17 citation statements)

References 98 publications

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“…In particular, our advanced understanding of factors influencing pharmacokinetics, together with the widespread use of structural biology and computational tools, now help us greatly to define design hypotheses concerning how properties can be optimised in a rational fashion (Ekins et al., 2010; Lusher et al., 2011; Plowright et al., 2012). Figure 9 describes the example of multi-objective optimization of PI3K inhibitors (Folkes et al., 2008; Raynaud et al., 2009).…”

Section: Lead Optimisation To Select a Clinical Candidatementioning

confidence: 99%

Discovery of small molecule cancer drugs: Successes, challenges and opportunities

2012

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The discovery and development of small molecule cancer drugs has been revolutionised over the last decade. Most notably, we have moved from a one-size-fits-all approach that emphasized cytotoxic chemotherapy to a personalised medicine strategy that focuses on the discovery and development of molecularly targeted drugs that exploit the particular genetic addictions, dependencies and vulnerabilities of cancer cells. These exploitable characteristics are increasingly being revealed by our expanding understanding of the abnormal biology and genetics of cancer cells, accelerated by cancer genome sequencing and other high-throughput genome-wide campaigns, including functional screens using RNA interference. In this review we provide an overview of contemporary approaches to the discovery of small molecule cancer drugs, highlighting successes, current challenges and future opportunities. We focus in particular on four key steps: Target validation and selection; chemical hit and lead generation; lead optimization to identify a clinical drug candidate; and finally hypothesis-driven, biomarker-led clinical trials. Although all of these steps are critical, we view target validation and selection and the conduct of biology-directed clinical trials as especially important areas upon which to focus to speed progress from gene to drug and to reduce the unacceptably high attrition rate during clinical development. Other challenges include expanding the envelope of druggability for less tractable targets, understanding and overcoming drug resistance, and designing intelligent and effective drug combinations. We discuss not only scientific and technical challenges, but also the assessment and mitigation of risks as well as organizational, cultural and funding problems for cancer drug discovery and development, together with solutions to overcome the ‘Valley of Death’ between basic research and approved medicines. We envisage a future in which addressing these challenges will enhance our rapid progress towards truly personalised medicine for cancer patients.

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Section: Lead Optimisation To Select a Clinical Candidatementioning

confidence: 99%

Discovery of small molecule cancer drugs: Successes, challenges and opportunities

2012

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“…They have also been widely applied in chemometrics and cheminformatics for many years, demonstrating that there is no lack of tools available to the drug designer wishing to quantitatively analyze big data. The challenge is therefore to ensure that the wealth of available tools are appropriately applied, in a timely fashion, to high-quality data within teams willing to incorporate new insight into their design strategies [11].…”

Section: Data Analytics For Quantitative Drug Designmentioning

confidence: 99%

Finding the Right Approach to Big Data-Driven Medicinal Chemistry

Lusher

Ritschel

2015

Future Med. Chem.

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“…Simply gathering, storing and making the data easily available to project scientists is a challenge for informatics platforms [57]. However, it is essential that software implementing MPO algorithms be intuitive and user friendly, as it should be possible for all decision makers to explore trade-offs in data and easily interpret the results, even if they are not computational experts.…”

Section: 100 Virtual Librarymentioning

confidence: 99%

Multi-Parameter Optimization: Identifying High Quality Compounds with a Balance of Properties

Segall

2012

CPD

136

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A successful, efficacious and safe drug must have a balance of properties, including potency against its intended target, appropriate absorption, distribution, metabolism, and elimination (ADME) properties and an acceptable safety profile. Achieving this balance of, often conflicting, requirements is a major challenge in drug discovery. Approaches to simultaneously optimizing many factors in a design are broadly described under the term 'multi-parameter optimization' (MPO). In this review, we will describe how MPO can be applied to efficiently design and select high quality compounds and describe the range of methods that have been employed in drug discovery, including; simple 'rules of thumb' such as Lipinski's rule; desirability functions; Pareto optimization; and probabilistic approaches that take into consideration the uncertainty in all drug discovery data due to predictive error and experimental variability. We will explore how these methods have been applied to predicted and experimental data to reduce attrition and improve the productivity of the drug discovery process.

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A molecular informatics view on best practice in multi-parameter compound optimization

Cited by 19 publications

References 98 publications

Discovery of small molecule cancer drugs: Successes, challenges and opportunities

Discovery of small molecule cancer drugs: Successes, challenges and opportunities

Finding the Right Approach to Big Data-Driven Medicinal Chemistry

Multi-Parameter Optimization: Identifying High Quality Compounds with a Balance of Properties

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