Sandexis Medicinal Chemistry Ltd.

Computational and medicinal chemistry consultants

Europe (non-EU)

English

Academic affiliation

Company of one computational and three medicinal chemists who spent 10 years together in big pharma and have been consulting for small pharma since 2011.

Contact

Individual

Company

Skills

High throughput screening

Lead optimization

Structure-based drug design

Linux/Unix

Ligand-based drug design

Clustering

Data mining (ChEMBL, etc.)

Covalent library design

About

Have worked with over 100 clients across the globe, including not-for-profit organisations, small biotechs and big pharma, supporting projects at all stages from target analysis to candidate nomination.

Sandexis currently have three consultants available for working on your projects, as introduced below.

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Gavin Whitlock

Experience of working with a diverse group of clients, from ground-breaking technology companies, not-for-profit consortia through to mid-sized pharma organisations, to deliver innovative medicinal chemistry design solutions, independent review of drug discovery projects, supporting new company generation, including:

• Instrumental in driving multiple early drug discovery projects for a UK biotech client. This includes carrying out project and target reviews, influencing screening strategy, identifying appropriate chemical starting points, leading the medicinal chemistry design in hit-to-lead and lead optimisation phases through to candidate nomination. Working with multiple CROs and academic collaborators to enable projects to reach appropriate milestones in a time- and cost-efficient manner.

• Integral member of multiple VC-funded start-up project teams; covering reviews of novel drug targets in oncology and neurodegeneration, leading the hit- finding strategies and subsequent hit-to-lead campaigns. Novel hit series identified and optimised to provide chemical equity that supported further investment in highly competitive areas.

• Key member of the project team, working with DNDi and its collaborators, to deliver innovative medicinal chemistry design, chemistry CRO management and input into compound progression across a portfolio of neglected disease drug discovery projects.

• Highly experienced in carrying out independent reviews of drug discovery projects for multiple clients, including large and mid-sized pharma companies, biotechs, investors and charitable research organisations. Contributed to over 100 reviews since 2011.

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Paul Glossop

Experienced medicinal chemist and collaborator, working with a diverse group of clients such as academic establishments, not-for-profit organisations, biotech companies and pharmaceutical organisations. Excellent communication skills working with virtual drug discovery teams that are geographically dispersed. Successfully delivered preclinical candidates, demonstrating continued impact through innovative medicinal chemistry, CRO management and decision making. Regularly prepared and presented scientific progress to Board of Directors (BoD) and conducted independent due-diligence reviews of drug discovery projects. Examples include:

• Long-standing consultant to a UK biotech company for the last six years, providing medicinal chemistry and drug discovery expertise to a portfolio of projects, including oncology indications and delivered an oral preclinical candidate.

• Key member of a University of Sheffield project team, seeking to develop AM₂-receptor antagonists for the treatment of pancreatic cancer. Inventor on three published patent applications and author on two publications arising from this collaboration.

• Regular collaborator across drug discovery project teams in biotech companies from the UK, Europe and US. Led medicinal chemistry design strategies, managed chemistry CROs and progressed lead compounds through the screening cascade and into candidate-enabling studies.

• Collaborated with DNDi (Switzerland) and its partners to deliver two preclinical candidates across a portfolio of neglected-disease drug discovery projects.

• Partnered with MMV (Switzerland) and DNDi on their initiative to develop the Pandemic Response Box, a collection of 400 compounds to facilitate drug discovery in emerging infectious diseases (especially relevant since the emergence of COVID-19).

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James Mills

Experienced computational chemist working with a large number of clients to provide support to projects at all stages. Level of involvement varies from providing extra bandwidth and complementing in-house computational chemistry to full support of discovery projects.

• Built up a suite of in-house programs and tools to carry out a variety of tasks ranging from 2D fingerprinting through to 3D overlap of ligands and proteins. Often write bespoke tools to meet needs of clients and enable a more flexible approach to problem solving.

• Created in-house versions of a number of compound databases to enable flexible interrogation and manipulation e.g. ChEMBL, and vendor catalogues like Mcule, MolPort, Enamine and eMolecules.

• Downloaded the PDB and converted to multiple databases that enable searching on the basis of text, ligand structure, protein sequence, binding pocket features or interactions.

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Work experience

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Education

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Publications

Novel Amino-pyrazole Ureas with Potent In Vitro and In Vivo Antileishmanial Activity

Rapid Discovery of a Novel Series of Abl Kinase Inhibitors by Application of an Integrated Microfluidic Synthesis and Screening Platform

Case STUDIES

HTS hit analysis and triage

HTS output data were analysed to enable selection of a more optimal set of compounds than the default cutoff-based compounds. In the case of a low-hit-rate screen, provisional hits above cutoff were supplemented by mining into the apparent HTS noise to pick up extra hits, either compounds with mid-range activity in low-performing wells (well effects in the HTS were identified, so this approach corrects for false negatives) or compounds that were scored highly by Bayesian models built from the full HTS dataset. Compound property and quality filters were used to remove compounds from the hitlist that would be deemed either unworthy of follow-up or that would likely be frequent hitters. Clustering and eyeballing of compounds was also used to reduce the redundancy of compounds, allowing the exploration of more chemotypes in the confirmation and concentration-response stages. These and other approaches allowed the identification of many more chemotypes than would have been identified by for example 3-sigma cutoffs alone.

Building screening collection

We were tasked with building a bespoke screening collection of ~20000 compounds that was to be biased towards a specific protein family. We analysed existing chemical matter known to be active at targets within this family and used this information to design a set of compounds that were similar in either chemical or property space. The library was designed to be diverse (limited number of compounds per scaffold), high quality (no red alerts and fewer examples of groups disfavoured by medicinal chemists) and a maximal size for the predefined budget.

Analysis of compound collections

A number of screening libraries are available for purchase. We were able to compare and contrast a selection of these libraries to enable an informed decision on which libraries to purchase. Factors included distributions of physicochemical properties, presence and absence of structural and PAINS alerts, complementarity to compounds already screened and chemical diversity.

Close-in mining

A client provided an initial set of hit compounds. Catalogues (e.g. MCule) were mined for compounds possessing similarity to these probes. The aim of the approach was to explore compounds that were similar in as many different ways as possible, so in addition to compounds with high maximum common substructure, we also looked for compounds with high 2D pharmacophore similarity, compounds that contain the same LHS or RHS, or that incorporate scaffold hops whilst retaining similar R groups in the periphery. The results were mapped back on to the HTS screen data used to generate the initial hits in order to ensure we were not covering space already inhabited by inactives. Up to 50 near neighbours per compound were selected for purchase and the resultant SAR enabled decision making between the initial hits and kickstarted the hit optimisation program.

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