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Avoiding Missed Opportunities by Analysing the Sensitivity of our Decisions

Wednesday, 19 October 2016 00:00
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Matt Segall

We've just submitted this article that describes how we can consider the impact of the compound selection criteria we choose on the decisions we make. Identifying sensitive criteria can highlight new avenues for exploration and potential missed opportunities.

Assessing the sensitivity of a criterion in a property profile

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Practical Application of Multi-Parameter Optimization to Guide Successful Drug Discovery

Friday, 27 March 2015 13:40
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Matt Segall
Dr Matt Segall, Optibrium, gave this presentation at the "Guiding Optimal Compound Design and Development Symposium" held in Cambridge, MA, USA on 19 March 2015.

Abstract
A high quality drug must exhibit a balance of many properties, including potency, ADME and safety. Multi-parameter Optimization (MPO) [1] methods guide the selection and design of compounds to identify those with the highest chance of success, while minimizing opportunities missed by inappropriately rejecting compounds. In drug discovery this is particularly challenging due to the complex, often conflicting nature of the property requirements, combined with uncertain data because of experimental variability or predictive error.

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Improving the Chance of Success Where an Outcome Can't be Predicted

Wednesday, 08 October 2014 10:44
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Matt Segall

Matt Segall gave this presentation at the ACS Fall 2014 National Meeting & Exposition held in San Francisco, USA on 10th August 2014.

Abstract

In silico models are widely used in drug discovery to predict key ADME properties for compounds before synthesis or testing in in vitro assays. Common models cover a wide range of endpoints including physicochemical properties, absorption, blood-brain barrier penetration and interactions with proteins including enzymes, transporters and ion channels. Complex in vivo endpoints, such as pharmacokinetic parameters or toxicity, are more challenging to predict confidently from compound structure because they arise from multiple mechanisms, each with their own structure-activity relationships. However, in cases where we can't confidently predict an outcome, it is still possible to identify compounds with an improved chance of achieving a good result for these objectives. We will describe how predictions of multiple, relatively simple compound properties can be combined in a multi-parameter optimisation framework to target compounds with an improved chance of success against complex in vivo endpoints. We will illustrate how property profiles can be derived for PK and toxicity objectives and applied in the context of drug discovery.


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The challenges of decision making using uncertain data

Wednesday, 08 October 2014 10:34
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Ed Champness

Ed Champness gave this presentation at the ACS FAll 2014 National Meeting & Exposition held in San Francisco, USA on 10th August 2014.

Abstract

The drug discovery process is one of multi-parameter optimisation, as we search for compounds that exhibit the right balance of properties necessary for them to become successful drugs. However, all the data we work with in drug discovery come from models, be they in vivo, in vitro or in silico, and are prone to experimental variability or statistical errors. Unfortunately, this information about the uncertainties in our data is often ignored during the decision making process. Here we highlight the dangers of not taking these errors into consideration as we prioritise and select compounds. We describe methodologies we can adopt to include this information in our prioritisation processes and highlight, with examples, the different conclusions we can reach as a result.

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Preprint: Advances in Multi-parameter Optimisation Methods for de Novo Drug Design

Monday, 07 April 2014 14:47
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Matt Segall
This review article has just been accepted by Expert Opinion in Drug Discovery and discusses recent developments in the methods and opinions in multi-parameter optimisation, focusing on applications to de novo drug design and illustrated with published examples.

BIOSTER transformation library

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Advances in Multi-Parameter Optimisation: Targeting the ‘best’ profile for your project’s objectives

Wednesday, 02 April 2014 10:15
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Ed Champness

Ed Champness gave this presentation at the ACS Spring 2014 National Meeting & Exposition held in Dallas, USA on 16th March 2014.

Abstract

Multi-parameter optimisation (MPO) has been widely adopted in drug discovery, to quickly target compounds with a balance of properties required for downstream success, including: potency against the intended target; absorption, distribution, metabolism and excretion (ADME) properties; and a reduced risk of toxicity. However, the increasing complexity of experimental data and calculated properties considered, even in early drug discovery, raises a challenge to determine the best profile with which to select compounds with a high chance of downstream success. We will describe recent developments that enable project teams to find and validate MPO profiles to identify the most important data and optimal selection criteria, with which to identify high quality compounds for their objectives. This enables synthetic and experimental resources to be prioritised to generate the most relevant compounds and data. We will illustrate this with example applications, including the selection of non-toxic compounds based on high dimensional in vitro assay data..

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Finding and Applying Multi-Parameter Rules to Guide Successful Drug Discovery

Monday, 24 March 2014 15:25
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Matt Segall
Matt Segall gave this presentation at the International Symposium on Compound Design Technologies held in Tokyo and Osaka, Japan on 19 and 20 March 2014.

Abstract
A high quality drug must exhibit a balance of many properties, including potency, ADME and safety. These are often expressed as property ‘rules’ that a compound must meet in order to progress. Applying these rules effectively in drug discovery is challenging due to the complex, often conflicting property requirements they reflect, combined with uncertain data because of experimental variability or predictive error. We will discuss how methods known as multi-parameter optimization (MPO) [1] are currently being applied to quickly target compounds with the best chance of success, while avoiding missed opportunities.

But, how do we know what the appropriate profile of property criteria might be to efficiently identify successful leads and candidate compounds for a specific project? The property criteria will depend on the ultimate goal of the project, e.g. therapeutic indication and route of administration, and are typically chosen based on the subjective opinion of the project team. However, analysis of historical data can help to guide the determination of the most appropriate profile, which can then be used prospectively to prioritise new compounds. We will describe how new methods, known as rule induction [2], can guide this process to identify multi-parameter rules that distinguish successful compounds for a chosen objective. The resulting rules are interpretable and modifiable, allowing experts to understand and adjust them based on their knowledge of the underlying biology and chemistry. Furthermore, the importance of each criterion can be identified, allowing the most important data to be prioritized to make effective compound prioritization decisions.

References: [1] M.D. Segall. Multi-Parameter Optimization: Identifying high quality compounds with a balance of properties. Curr. Pharm. Des. (2012) 18(9) pp. 1292-1310; [2] I. Yusof, F. Shah, N. Greene and M.S. Segall. Finding the Rules for Successful Drug Optimization. Drug. Discov. Today (2014) (in press).

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Preprint: Finding the Rules for Successful Drug Optimization

Thursday, 21 November 2013 12:10
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Matt Segall

We recently submitted this article, co-authored with Nigel Greene and Falgun Shah of Pfizer's compound safety prediction group.  In the article, we discuss new 'rule induction' methods that explore complex data to find interpretable, multi-parameter rules, tailored to any drug discovery objective that can be used to identify compounds with a higher chance of success. This is illustrated with applications to simple ‘drug like’ properties for oral drugs and exploration of experimental target inhibition data to find rules for selecting compounds with a low risk of cardio- and hepatotoxicity.

Rule induction

 

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Finding Multi-parameter Rules for Successful Optimization

Thursday, 18 April 2013 14:39
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Matt Segall

Matt gave this presentation at the ACS National Spring Meeting 2013 in New Orleans.

Abstract

Multi-parameter optimization (MPO) is increasingly used in drug discovery to prioritise compounds against a profile of properties required to succeed. But, how do we know what profile to use? The property criteria will depend on the ultimate objective of the project and are typically based on the subjective opinion of the project team.

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Multi-parameter Optimisation in Drug Discovery:

Friday, 30 September 2011 00:00
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Matt Segall

Matt gave this presentation at ELRIG Drug Discovery 2011.

Abstract

A high quality drug must exhibit a balance of many properties, including potency, ADME and safety. Identifying an optimal solution that balances multiple factors is known as ‘multi-parameter optimisation’ (MPO). In drug discovery this is particularly challenging due to complex, often conflicting property requirements combined with uncertain data because of experimental variability or predictive error. These make it difficult to decide with confidence which lines of enquiry to pursue and which compounds to prioritise. We will review recent developments in MPO to guide decision-making during hit-to-lead and lead optimisation. We will present a flexible approach that allows project-specific property criteria and their weights to be easily defined and integrates the available data to provide an overall compound score, explicitly taking uncertainty into account. These scores can be used to prioritise compounds with the highest chance of success while mitigating risk by exploring a diverse range of possible chemistries.

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Preprint: Multi-Parameter Optimisation Review Article

Tuesday, 06 September 2011 10:04
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Matt Segall

This review article, "Multi-Parameter Optimization: Identifying high quality compounds with a balance of properties" was published in Current Pharmaceutical Design (2012) 18(9) pp. 1292--1310. In it, we survey the range of methods used for MPO in drug discovery, compare their strengths and weaknesses and present some example applications.

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