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Lead Identification: Where Science Meets Art

Tuesday, 30 May 2017 13:20
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Mehran Jalaie
Dr Mehran Jalaie, Pfizer, gave this presentation at "Streamlining Drug Discovery" symposium held in San Diego, CA, USA on 21 April 2017.

Abstract
Picking desirable bioactive compounds for therapeutic projects has been an “artful skill” for decades relying on intuition and previous experience of individuals involved in compound selections on active projects. However, as the frequency of novel targets rises, previous experience on unrelated targets becomes increasingly less likely to be helpful. In recent years, several teams have developed many virtual screening techniques to assist in hit identification, with scoring functions and statistical analysis providing scientific backing and supplementation to designer’s intuitions.
In this presentation, we will share the results of virtual screening campaigns that successfully identified hits and lead series for several projects. The success and efficiency of the lead identification efforts for these projects were driven by availability, sophistication, streamlining of the tools, processes, and the incorporation of best practice methodologies.

You can download this presentation as a PDF.


Resolving the question of on- or off-target toxicity - a case study

Tuesday, 30 May 2017 13:08
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Joachim Rudolph
Dr Joachim Rudolph, Genentech, gave this presentation at "Streamlining Drug Discovery" symposium held in San Diego, AA, USA on 21 April 2017.

Abstract
The question of whether toxicity caused by small molecules is due to on-target or off-target pharmacological effects is frequently encountered in drug discovery programs and of decision making significance. Genetically engineered mouse models are widely used to assess the safety consequences of depleting a target, but have limitations in predicting the toxic effects of target-related small molecule drug action. Diverse chemical analogs were designed/selected to investigate the root cause of cardiovascular toxicity encountered in a program aimed at discovering inhibitors of pan-Group I p21-activated kinases (PAK1, 2, and 3) for use in breast cancer. Mouse tolerability studies with these compounds revealed persistent toxicity, a correlation of minimum toxic concentrations and PAK1/2 mediated cellular potencies, and absence of toxicity with structural analogs devoid of PAK1/2 activity. Our data suggest that the toxicity results from the inhibition of PAK2, which may be enhanced by PAK1 inhibition, and caution against continued pursuit of pan-Group I PAK inhibitors in drug discovery. Carefully selected sets of small molecule tool compounds have significant value in probing the safety of drug targets, and their use in tolerability experiments is highly complementary to gene knockout studies.

You can download this presentation as a PDF.


Supporting Compound Optimisation in Not-for-Profit and Academic Research

Tuesday, 09 May 2017 10:41
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Matt Segall

Matthew Segall1, Tamsin Mansley1, Peter Hunt1, Kelly Chibale2, Tanya Paquet2, James Duffy3
1 Optibrium Limited, Cambridge, UK
2 University of Cape Town, Cape Town, South Africa
3 Medicines for Malaria Ventures, Geneva, Switzerland

This presentation was given at the ACS Spring 2017 National Meeting & Exposition held in San Diego, USA.

Abstract

The not-for-profit and academic sectors have become important sources of novel drug candidates, particularly for neglected and developing world diseases or niche indications. Drug discovery projects in these sectors are often conducted on a collaborative basis, pooling resources and experience across multiple research groups and using contract research organisations as appropriate. Several software platforms have been developed to facilitate the secure sharing of data across organisations, but in this talk we will discuss software approaches that focus on using these data to guide decisions regarding the selection and design of high quality compounds.

Malaria Infection Cycle
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Confidently Targeting High Quality Hits from High-Throughput Screening

Tuesday, 09 May 2017 10:25
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Matt Segall

Matt Segall gave this presentation at the ACS Spring 2017 National Meeting & Exposition held in San Diego, USA.

Abstract

When analysing the results from a high throughput screening (HTS) campaign the goal is to identify diverse hit series with high activity, structure-activity relationships (SAR) that indicate the opportunity for further optimisation and good ‘lead like’ properties. The common practise is to apply filters to these large datasets, for example an activity threshold or simple properties such as molecular weight, logP, numbers of hydrogen bond donors and acceptors or the presence of substructures that may indicate non-specific binding. However, this process draws artificially harsh distinctions between compounds, given the inherent variability in HTS data and the low correlation between simple properties and the ultimate in vivo disposition of a compound. This leads to selection of ‘false positives’, i.e. active compounds that are not good starting points for further optimisation and ‘false negatives’, i.e. potentially good compounds that have been inappropriately rejected. We will illustrate how a true multi-parameter approach enables appropriate weight to be given to these data to confidently identify high quality, potent hits while avoiding missed opportunities.

Mapping this information across the chemical diversity of the compounds explored in an HTS campaign, by clustering or visualisation of a ‘chemical space’, helps to find ‘hot spots’ representing high quality series of compounds for further investigation while also considering diverse chemistries to provide potential backup series. Finally, exploring the SAR within these series then helps to identify further opportunities for optimisation. We will show how this can all be achieved in a high visual and intuitive way, to move quickly and confidently from initial HTS hits to high quality lead series.
HTS Network

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Data visualization: Saying it all in a bite-sized chunk

Tuesday, 09 May 2017 10:19
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Edmund Champness

Ed Champness gave this presentation at the ACS Spring 2017 National Meeting & Exposition held in San Diego, USA.

Abstract

We often use the term “data visualisation” to refer to the creation of plots that enable us to represent tables of numbers in an easily-digestible form and yet we use many visual approaches for representing compounds, targets, assay results, model predictions, etc. The combination of these varied representations, containing many dimensions of data, presents us with an interesting challenge as we seek to understand what they are telling us and then convey our conclusions to others. Something to keep in mind is that while we may keep our social and professional lives separate, the boom in social media is a pretty clear indicator about the way we like to consume information, and there’s no reason this should change just because we walk through the office door. While it is easy to simplify the overall picture by simply trimming off the detail, how can we ensure that the “bite-sized visualisation” we ultimately create is an appropriate reflection of the underlying information such that it won’t inappropriately bias our decisions? We will illustrate some of the ways that we can achieve this and discuss visual methods to guide our decisions in drug discovery.

You can download this presentation as a PDF.


Crystal structures, binding interactions, and ADME evaluation of brain penetrant N-substituted indazole-5-carboxamides as subnanomolar, selective monoamine oxidase B and dual MAO-A/B inhibitors

Thursday, 26 January 2017 11:07
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Dr. Nikolay Tzvetkov

This paper was recently accepted by EurJMedChem and describes the use of SeeSAR and StarDrop's ADME models to guide the design of potent, selective MAO-A and -B inhibitors with appropriate ADME properties for the treatment of Parkinson's disease and other neurological disorders.

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Practical Applications of Matched Series Analysis

Tuesday, 06 December 2016 17:52
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Peter Hunt

This paper, co-authored with our colleagues at NextMove Software, has just been accepted for publication in Future Med. Chem. and explores applications of Matched Series Analysis to SAR transfer, binding mode suggestion, and data point validation.

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Medicinal Chemistry is an art, when you don’t understand the data

Tuesday, 03 May 2016 16:18
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Jeremy Edmunds
Dr Jeremy Edmunds, Abbvie, gave this presentation at "Streamlining Drug Discovery and Development" held in Cambridge, MA, USA on 11 April 2016.

Abstract
When one considers the considerable expense that is associated with developing a drug, it is clearly the responsibility of the chemist to ensure that they are preparing the most optimal compound. To achieve this we have focused our efforts within Abbvie medicinal chemistry toward excellence in design and excellence in synthesis. Here he describes the trials and tribulations of this approach.

You can download this presentation as a PDF.


Speeding up and improving the Identification of a potent B2 agonist as a growth promoter for cattle

Friday, 15 April 2016 16:46
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Ashley Fenwick
Dr Ashley Fenwick, Zoetis, gave this presentation at "Streamlining Drug Discovery and Development" held in Cambridge, MA, USA on 11 April 2016.

Abstract
Being spun out of a large pharmaceutical company and losing access to a full suite of programs and aids to support drug discovery, Zoetis has had to build its infrastructure from scratch. A monumental task, but also a once in a life time opportunity to change how we do things. By looking back at a project completed prior to the separation, the advantages offered by the suit of programs and solutions we now have in place becomes apparent and paints an encouraging picture of the future.

You can download this presentation as a PDF.


Cheminformatics from the end-user perspective: Past, present and future

Friday, 15 April 2016 16:41
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Nick Foster
Dr Paul Greenspan, Takeda, gave this presentation at "Streamlining Drug Discovery and Development" held in Cambridge, MA, USA on 11 April 2016.

Abstract
Over the course of my 25 year career as a medicinal chemist, cheminformatics (or what we now call cheminformatics) has evolved from rudimentary chemistry databases, to highly sophisticated software suites with ever more powerful means of visualizing and analyzing large chemistry-rich datasets. At the same time, the proliferation of data generation across a wide array of biological and physical parameters, and the availability of ever larger compound collections, has created an explosion in the volume and breadth of data that is available to the drug designer. With both of these trends likely to continue, we are persistently confronted with a fundamental question: How do we make the best use of all of the data that we have at our disposal? My presentation will attempt to review this evolution from the perspective of an end-user, highlighting the opportunities and challenges that we still face as we seek to continually refine the quality of our decision-making in choosing what molecules to make.

You can download this presentation as a PDF.


Structure Guided Design and Optimization of Selective Kinase Inhibitors from Fragment Starting Points

Friday, 15 April 2016 16:37
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Nick Foster
Dr Steve Woodhead, Takeda, gave this presentation at "Streamlining Drug Discovery and Development" held in San Francisco, CA, USA on 14 April 2016.

Abstract
Over recent years the kinome has provided a rich source of druggable therapeutic targets, with over 25 kinase inhibitors now on the market and many more undergoing clinical evaluation. That said, there remain significant challenges to overcome in kinase drug discovery. For example, poor physicochemical properties and non-mechanism based toxicity, often arising from broader kinome activity, are frequently responsible for attrition during development. Accordingly, specificity for the desired therapeutic target and well optimized physicochemical and pharmaceutical properties are crucial for increasing the overall likelihood of success.

Fragment Based Drug Discovery (FBDD) has firmly established itself as a productive approach to the discovery of small molecule drugs and, when supported by X-ray crystallography, can offer a unique platform from which to optimize molecules with both attractive physicochemical property profiles and a high degree of specificity for the target of interest. This presentation will describe the use of FBDD and iterative structure based design to deliver selective small molecule inhibitors for two kinase targets, whilst maintaining desirable physicochemical properties.

You can download this presentation as a PDF.


TB Alliance Drug Discovery and Development: Harnessing Global Resources to Address a Global Disease

Friday, 15 April 2016 16:24
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Nick Foster
Dr Chris Cooper, TB Alliance, gave this presentation at "Streamlining Drug Discovery and Development" held in San Francisco, CA, USA on 14 April 2016.

Abstract
TB is a leading cause of mortality and morbidity globally, killing 1.4 million people every year,1 and robbing millions more of health, hope, and prosperity. Current TB regimens are highly inadequate requiring 6-24 months to complete treatment. Protracted treatment times result in poor adherence and consequently promote the development of multi-drug resistant (MDR) and extensively drug resistant (XDR) TB. Treatment options for drug resistant TB are complex, toxic, and expensive, with less than 10% of MDR TB patients receiving proper care, and of those, more than a third failing to be fully cured.

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Webinar: The Magic Behind SeeSAR

Thursday, 07 April 2016 12:31
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Nick Foster

Read the presentation "The Magic Behind SeeSAR™: Visual, Interactive 3D Lead Optimisation for Anyone" from the joint BioSolveIT/Optibrium Webinar on April 6, 2016. In this presentation Marcus Gastreich of BioSolveIT described the technology underlying their HYDE scoring function and SeeSAR. This also included worked examples to demonstrate how visually informed lead optimisation can save you considerable time, leading to compounds with an improved profile.

SeeSAR

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Bridging the dimensions: Seamless integration of 3D structure-based design and 2D structure-activity relationships to guide medicinal chemistry

Wednesday, 23 March 2016 16:46
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Matt Segall

Matt Segall gave this presentation at the ACS Spring National Meeting & Exposition held in San Diego, USA on 13th March 2016.

Abstract

The effective use of software can have a major impact on timelines and innovation in drug discovery. However, the traditional split between computational modellers and synthetic chemists has been blurred and software must be accessible across disciplines to quickly understand and predict structure-activity relationships (SAR). There has been a similar divide between tools for three-dimensional (3D) structure-based design and those for analysis of SAR based on a two-dimensional (2D) compound structure. Seamless integration between these approaches would enable all of the available structural knowledge to be used to guide the efficient design of high quality, active compounds.

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Closing the Loop Between Synthesis and Design

Monday, 21 March 2016 15:46
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Tamsin Mansley

Tamsin Mansley gave this presentation at the ACS Spring National Meeting & Exposition held in San Diego, USA on 13th March 2016.

Abstract

Chemists frequently draw upon their experience and chemical intuition to make sense of complex project data and select new compounds to synthesize. However, drug discovery projects increasingly demand greater efficiencies with shorter timelines and lower costs, putting medicinal chemists under pressure. Additionally, the traditional divide between computational modellers and synthetic chemists is no longer clear and software must be easily accessible across disciplines; project teams need to quickly understand and predict structure-activity relationships (SAR), identify potential liabilities and design new compounds with the highest chance of success.

Synthesis to Design

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Data visualization: New directions or just familiar routes?

Tuesday, 25 August 2015 13:27
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Ed Champness

Ed Champness gave this presentation at the ACS Fall 2015 National Meeting & Exposition held in Boston, USA on 19th August 2015.

Abstract

Data visualization tools make it very easy to represent our data graphically and present it in a way that clearly communicates patterns and trends. But, there is a risk that visualizations may be used, in practice, to confirm or justify our own hypotheses and biases. Instead, can data visualizations bring to light patterns in our data, drive new hypotheses and show us things we weren’t expecting? In this presentation we will look at a number of common data analyses and visualizations used within the drug discovery process. We will illustrate some of the ways that these approaches can be misleading, with examples showing how inappropriate use of data visualization can lead us to conclusions which aren’t necessarily supported by our data. We will discuss alternative, visual methods to guide our decisions in drug discovery and consider ways in which these can enable us to drive the analysis of data without introducing any of our own biases.
Data Visualisation: 5HT1a Example

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Webinar: Beyond Matched Pairs

Wednesday, 15 April 2015 09:20
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Nick Foster

Read the presentation "Beyond Matched Pairs: Applying Matsy to predict new optimisation strategies" at the joint NextMove Software/Optibrium Webinar on April 14, 2015. In this Noel O'Boyle described the use of the 'Matsy' algorithm to make predictions of new structures with a high probability of increasing target activity, based on statistical analysis of matched series within a large matched series data set. He also described the use of matched series analysis for SAR transfer and compared the results of the Matsy predictions with Topliss decision trees. Ed Champness described how Matsy will be applied in StarDrop's Nova module, which automatically generates new compound structures to stimulate the search for optimisation strategies related to initial hit or lead compounds.

Example of Matsy output

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Predicting Adverse Drug Reactions: What Works and What Doesn’t

Friday, 27 March 2015 14:00
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Nigel Greene
Dr Nigel Green, Pfizer, gave this presentation at the "Guiding Optimal Compound Design and Development Symposium" held in Cambridge, MA, USA on 19 March 2015.

Abstract
The ability to predict the adverse safety effects of small molecules has long been an aspiration in the pharmaceutical industry as it offers the advantage of lowering costs associated with the identification of candidate drugs whilst increasing the speed of development. Numerous methods and approaches to predicting adverse events have been developed but computational models that utilize physicochemical properties, structural alerts, polypharmacology assessments and mechanistic in vitro assays offer the most promise. One such approach is now being used to help guide early medicinal chemistry efforts and can be built into product development strategies to mitigate unwanted health effects in drug candidates that ultimately go to commercialization. By combining these diverse data types in an optimized, holistic model, a prediction of the exposure at which a compound may demonstrate a threshold level of toxicity in an in vivo study can be made. By combining this prediction with assessments of projected efficacious concentrations some success has been achieved in predicting the likely therapeutic index of a novel molecule. The use of such approaches allows medicinal chemists to steer early design efforts away from unproductive space, potentially reducing the use of in vivo experimentation for compounds with no hope of success.

You can download this presentation as a PDF.


Understanding Compound Quality

Friday, 27 March 2015 13:43
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Paul Leeson
Dr Paul Leeson, Paul Leeson Consulting Ltd, gave this presentation at the "Guiding Optimal Compound Design and Development Symposium" held in Cambridge, MA, USA on 19 March 2015.

Abstract
A significant body of data suggests that controlling the molecular properties of hits, leads and drug candidates in the drug discovery phase should help to reduce risks of poor drug metabolism and toxicity. Concern for the health of pharmaceutical pipelines stems from analyses of small molecules patented by the industry, which are on average more lipophilic and larger than approved oral drugs. This is relevant too because only ~4% of candidate drugs reach the market and it has been acknowledged that compound-related risks, in dose, exposure and toxicity, can be carried from discovery into more costly clinical development.

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Analyzing Selectivity Through Multi-dimensional Activity Cliff Analysis

Friday, 27 March 2015 12:22
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Tim Cheeseright
Dr Tim Cheeseright, Cresset, gave this presentation at the "Guiding Optimal Compound Design and Development Symposium" held in Cambridge, MA, USA on 19 March 2015.

Abstract
During lead optimization the stepwise progression of compound activity is often disrupted by compounds that cause a disproportionately large (positive or negative) change in the biological response. These activity cliffs have long been recognized as an important source of information about the requirements of the protein for the series of interest. Activity cliff analysis has traditionally been done in 2D, but we have developed methods for expanding the dimensionality of activity cliff detection to include the 3D shape and electrostatic character of the ligands. In contrast to fingerprint similarity methods, accurate 3D similarity methods treat bioisosteres correctly which allows the identification of cliffs which the 2D methods fail to find.

The detection of activity cliffs for the primary activity end point is a valuable addition to the arsenal of drug discovery scientists. However, modern drug discovery rarely proceeds through the optimization of a single end point. More often project teams are tasked with optimizing the primary activity while minimizing the effect on a secondary, selectivity target or on a critical ADMET parameter. We have therefore studied the application of the 3D activity cliff analysis to multiple activity endpoints. These ‘selectivity cliffs’ highlight where molecular changes have a large effect on the activity against one target but not another. I will discuss the challenges of visualizing this data and present some novel techniques to deal with this.

You can download this presentation as a PDF.


Preprint: Breaking Free from Chemical Spreadsheets

Thursday, 29 January 2015 09:39
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Matt Segall

We've just submitted this article, that explores the benefits of a more intuitive and flexible approach to viewing and interacting with drug discovery data. We illustrate how this can help to quickly identify high quality compounds and strategies for further compound optimisation.

Linked cards illustrating optimisation flow

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Beyond Matched Pairs

Tuesday, 23 December 2014 08:36
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Noel O'Boyle

Noel O'Boyle of NextMove Software gave this presentation "Beyond Matched Pairs: Using matched series for activity prediction" at our Drug Discovery Consultants' Day in November 2014. In this he described the use of the'Matsy' algorithm to make predictions of new structures with a high probability of increasing target activity, based on statistical analysis of matched series within a large matched series data set. He also described the use of matched series analysis for SAR transfer and compared the results of the Matsy predictions with Topliss decision trees.

Example of Matsy output

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Integrated predictive ADME tools for optimising exposure and safety in drug discovery and development

Thursday, 18 December 2014 10:28
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Jianling Wang
Dr Jianling Wang gave this presentation at the International Symposium on Compound Design Technologies held in Shanghai, China on 21 November 2014.

Abstract
The high attrition rate in drug development and the deteriorated drugability as a result of the shifted chemical space of new therapeutic target for unmet medical needs have posed drastic challenges in current drug discovery and development. It has triggered the strategic transition in the past decade into parallel assessment of efficacy and comprehensive ADMET (absorption, distribution, metabolism, elimination and toxicity)/DMPK (drug metabolism and pharmacokinetics) properties of new chemical entities (NCEs) in the lead selection and optimization stages, to convert chemically a problematic NCE to an ‘all-around’ candidate. The emergence of such comprehensive in silico, in vitro and in vivo ADMET/DMPK tools is, by no means, indicative of the game being over, as the “more is better” type of “box-checking” profiling strategy is no longer viable and frequently leads to suboptimal productivity. This presentation will focus on the intelligent integration of comprehensive in silico, in vitro and in vivo ADMET/PK data and routine application of hypothesis testing, enabling to diagnose the causative within the interplay among multiple ADMET/DMPK properties, and to project the benefits over risks of a drug candidate in clinic. An overview of existing tools will be presented along with selected case studies.

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Structure-based drug discovery in Shanghai Hengrui

Thursday, 18 December 2014 10:08
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Jerry Hu
Dr Qiyue (Jerry) Hu gave this presentation at the International Symposium on Compound Design Technologies held in Shanghai, China on 21 November 2014.

Abstract
Hengrui Pharmaceutical Co. is one of the top research-based Chinese pharmaceutical companies. Shanghai Hengrui is its major R&D center. My talk will use data from real drug discovery projects to illustrate how Structural-Based Drug Discovery (SBDD) is practiced in Shanghai Hengrui.

SBDD at Hengrui

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Novel lead optimization strategy of BACE I inhibitors for the treatment of Alzheimer’s disease by QSAR and PBPK modeling

Tuesday, 11 November 2014 12:57
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Jinju Byeon
This poster was presented by Jinju Byeon, Professor Young Shin and co-authors from Chungnam National University at the 2014 Pharmaceutical Society of Korea Meeting.

Introduction

Lead optimization is one of the most critical stages of drug discovery. The conventional lead optimization process normally starts with the identification of hit compounds which show decent Ki or IC50 for target proteins. Once identified, hundreds or thousands of derivatives are further synthesized to improve ADME(Absorption Distribution Metabolism Excretion)/PK(Pharmacokinetics) properties without compromising potency. However, this requires significant amounts of DMPK(Drug metabolism and Pharmacokinetics) resources and time due to the various in vitro ADME assays/in vivo PK studies that must be evaluated. Therefore, several in silico approaches have been recently introduced to predict physicochemical properties and ADME properties in a high throughput manner for quick ranking-ordering of compounds by several pharmaceutical scientists using in-house models and global models supplied by commercial software. In this study, we introduce an innovative in silico-based high throughput lead optimization strategy with QSAR and PBPK modelings using StarDrop™, ADMET predictor® and GastroPlus®.

You can download a PDF of this poster.


Visualising Structured Compound Data in an Unstructured Way

Sunday, 10 August 2014 11:21
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Nick Foster

This Poster was presented at the ACS Fall 2014 National Meeting & Exposition held in San Francisco, USA on 12th August 2014.

Abstract

When we view compound data sets, we almost always find ourselves looking at a ‘chemical spreadsheet’ or a ‘form view’ of compounds and data in a long list. However, compounds have much more complex relationships that can’t be captured in a single, sequential order. We present an alternative view, implemented in the StarDrop™ software [1], in which compound structure and associated data are presented on cards that can be moved and organised freely. This allows chemists to explore their data in a flexible and interactive way and to organise their data based on their perspective and thought processes. Algorithms, such as clustering or matched pair algorithms, can also be applied to organise compounds to draw out interesting patterns or features.

CardView
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Predictive Application of Bioisostere Transformations to Identify Novel High Quality Compound Ideas

Wednesday, 02 April 2014 10:21
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Ed Champness

Ed Champness gave this presentation at the ACS Spring 2014 National Meeting & Exposition held in Dallas, USA on 17th March 2014.

Abstract

We will describe how the principle of bioisosterism can be applied, in combination with predictive modelling and multi-parameter optimisation, to quickly search for new, high quality compound ideas and optimisation strategies. Bioisosteres are functional groups which have similar physical or chemical characteristics and hence similar biological effects. The relationships between bioisosteres may be encoded as molecular transformations and automatically applied to new compounds to generate novel compound structures that are likely to preserve the required biological activities. In silico models can be applied to predict the properties of the resulting structures, such as ADME and physicochemical characteristics. These data can, in turn, be integrated using a multi-parameter optimisation approach to prioritise those ideas that are most likely to achieve a required property profile. To illustrate this, we will discuss how the BIOSTER™ database of >20,000 precedented bioisostere transformations and molecular modifications resulting from alternative analogue design techniques [1] has been integrated with the StarDrop™ software [2]. We will describe example applications in drug discovery, including lead hopping and patent protection. Alogliptin: Retrospective Example

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Addressing Toxicity Risk when Designing and Selecting Compounds in Early Drug Discovery

Wednesday, 02 April 2014 10:02
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Ed Champness

Ed Champness gave this presentation at the ACS Spring 2014 National Meeting & Exposition held in Dallas, USA on 19th March 2014.

Abstract

It has been estimated that toxicity accounts for approximately 30% of expensive, late stage failures in development. Therefore, identifying and prioritising chemistries with a lower risk of toxicity, as early as possible in the drug discovery process, would help to address the high attrition rate in pharmaceutical R&D. We will describe how expert knowledge-based prediction of toxicity can alert chemists if their proposed compounds are likely to have an increased risk of causing toxicity, based on precedence for similar compounds where experimental data are available. However, an alert for potential should be given appropriate weight in the selection of compounds. It is important to balance potential opportunities against the risk of late stage failures caused by toxicity; an alert may not be sufficient reason to ‘kill’ a compound or chemical series. If a series achieves good outcomes for other requirements, it may be appropriate to progress selected compounds and generate experimental data to confirm or refute a prediction of potential toxicity. We will discuss how multi-parameter optimisation approaches can be used to balance the potential for toxicity with other properties required in a high quality candidate drug, such as potency and appropriate absorption, distribution, metabolism and elimination (ADME). Furthermore, it may be possible to modify a compound to reduce its likelihood of toxicity and we will describe how information on the region of a compound that triggers a toxicity alert can be interactively visualised to guide this redesign.

You can download this presentation as a PDF.


The Significance of Protein Structure Data Set Choices for in-silico Drug Discovery: Design of BACE1 Inhibitors

Monday, 24 March 2014 15:39
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Yoshio Hamada
Dr Yoshio Hamada gave this presentation at the International Symposium on Compound Design Technologies held in Tokyo and Osaka, Japan on 19 and 20 March 2014.

Abstract
In this lecture, I will discuss the significance of protein X-ray crystal structure data set choices for in-silico drug design/screening, using our drug discovery research on BACE1 inhibitors as an example. β-Secretase, also called BACE1 (β-site APP amyloid precursor protein cleaving enzyme 1) is a molecular target for developing Alzheimer’s disease (AD) drugs. BACE1 triggers the formation of amyloid β peptide (Aβ), which is the main component of senile plaques in the brains of AD patients, and is recognized as the causative agent of AD. BACE1 recognizes the EVKM*D sequence and cleaves amyloid precursor protein (APP) on the N-terminal side of the Aβ domain to produce Aβ. Swedish-mutant APP is found in familial AD patients and its cleavage site is mutated to the EVNL*D sequence, which is cleaved faster than the wild-type sequence is by BACE1. Ghosh et al reported the first X-ray crystal structure (1FKN) of BACE1 in a complex with an inhibitor (OM99-2) that was designed based on the Swedish-mutant sequence. The 1FKN structure showed that the Arg235 side chain of BACE1 interacted with the P2 side chain (Asn) of OM99-2 by hydrogen bonding. Many researchers, including our group, have reported BACE1 inhibitors that are based on the Swedish-mutant sequence and are designed using this crystal structure data set for docking calculation. However, we previously reported that most inhibitors complexed with BACE1, with the exception of OM99-2, and interacted with the Arg235 side chain of BACE1 by quantum chemical interactions such as σ-π interactions and not by hydrogen bonding. Furthermore, I found that such quantum chemical interactions are important for BACE1 inhibition. These findings indicated that the concepts for designing substrates and inhibitors are fundamentally different. Therefore, I proposed an “electron donor/acceptor bioisostere” medicinal science concept based on quantum chemical interactions, and applied it to design the first peptides with BACE1 inhibitory activity.

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Preprint: Addressing Toxicity Risk when Designing and Selecting Compounds in Early Drug Discovery

Friday, 01 November 2013 11:18
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Matt Segall

We've just submitted this article, co-authored with Chris Barber, CSO of Lhasa Limited. In it, we discuss how application of expert knowledge-based predictions of toxicity can be used with multi-parameter optimisation, to guide the design and selection of high quality compounds with a reduced risk of toxicity, early in the drug discovery process.

Toxicity chemical space and Glowing Molecule

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Applying Bioisosteric Transformations to Predict Novel, High Quality Compounds

Thursday, 03 October 2013 13:18
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James Chisholm

In this publication we describe how the principle of bioisosterism can be applied, in combination with predictive modelling and multi-parameter optimisation, to quickly search for new, high quality compound ideas and optimisation strategies.  Bioisosteres are functional groups which have similar physical or chemical characteristics and hence similar biological effects. The relationships between bioisosteres may be encoded as molecular transformations and automatically applied to new compounds to generate novel compound structures that are likely to preserve the required biological activities. In silico models can be applied to predict the properties of the resulting structures, such as ADME and physicochemical characteristics. These data can, in turn, be integrated using a multi-parameter optimisation approach to prioritise those ideas that are most likely to achieve a required property profile. To illustrate this, we will discuss how the BIOSTER™ database of >20,000 precedented bioisostere transformations and molecular modifications resulting from alternative analogue design techniques has been integrated with StarDrop. We will describe example applications in drug discovery, including lead hopping and patent protection.

Download a pre-print here


Exploring the chemical space of screening results

Monday, 29 April 2013 00:00
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Ed Champness

Ed Champness gave this presentation at the ACS National Spring Meeting 2013.

Abstract

When faced with the results from a screening campaign it is essential use this data to quickly focus on the best chemistries for progression. In this presentation we will describe two techniques for visualising a  'chemical space' to guide this exploration. We will demonstrate how these can be used to identify activity 'hotspots' and focus on these for detailed analysis of structure-activity relationships. This approach can also help to spot singletons and outliers that may represent false positives or negatives for further investigation. Furthermore, it is well understood that high quality chemistry will have not only good activity, but also appropriate absorption, distribution, metabolism, elimination and toxicity (ADMET) properties. We will show how data from multiple sources can be combined to select compounds for further study with an appropriate balance of activity, ADMET properties and structural diversity to mitigate downstream risk.

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Implementation of multi-criteria decision making (MCDM) tools in early drug discovery processes

Monday, 29 April 2013 00:00
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Marie Ledecq

Marie Ledecq from UCB Pharma gave this presentation at the ACS National Spring Meeting 2013.

Abstract

The current trend in medicinal chemistry is to focus on high quality ligands from the early beginning of the drug design process in order to reduce the drug attrition rate in later stages. Based on this assessment, medicinal chemistry practices are evolving; starting from potency centered drug design strategies towards a much more integrated vision where critical properties have to be optimized in parallel.

From this perspective, some specific MCDM tools can be used to discover better balanced lead compounds. These tools include the use of Derringer's desirability functions, and Pareto front based optimizers. In this presentation, it will be shown how these tools can be implemented to be used at several levels of the drug design process: to follow project progression and take enlightened decisions about series, and to help in the data analysis and the design of new compounds.

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Decision Support in Discovery through StarDrop

Tuesday, 11 September 2012 00:00
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Vijay Gombar

Dr Vijay Gombar gave this presentation at the StarDrop User Group Meeting and Workshop during the ACS National Fall Meeting 2012.

Summary

Vijay presented some examples of the ways that he and his colleagues have been using StarDrop in their projects at Eli Lilly since 2005.

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Preprint: Applying Med Chem Transformations and MPO to Guide the Search for High Quality Leads and Candidates

Friday, 21 October 2011 12:29
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Matt Segall

This is a preprint of an article that will appear in J. Chem. Inf. Model. http://dx.doi.org/10.1021/ci2003208.

This describes the concepts and algorithms underlying StarDrop's Nova module.

Abstract

In this article we describe a computational method that automatically generates chemically relevant compound ideas from an initial molecule, closely integrated with in silico models and a probabilistic scoring algorithm to highlight the compound ideas most likely to satisfy a user-defined profile of required properties. The new compound ideas are generated using medicinal chemistry ‘transformation rules’ taken from examples in the literature. We demonstrate that the set of 206 transformations employed is generally applicable, produces a wide range of new compounds and is representative of the types of modifications previously made to move from lead-like to drug-like compounds. Furthermore, we show that more than 94% of the compounds generated by transformation of typical drug-like molecules are acceptable to experienced medicinal chemists. Finally, we illustrate an application of our approach to the lead that ultimately led to the discovery of Duloxetine, a marketed serotonin reuptake inhibitor.

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Poster: Guided Application of Med Chem Rules to Generate Good Ideas

Thursday, 07 July 2011 00:00
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Matt Segall

Matt presented this poster at RICT in July 2011.

Abstract:

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Computational tools can guide the selection of high quality compounds, with a good balance across multiple properties, and guide strategies to design improved compounds. But, can software propose ideas for better compounds? We will demonstrate an approach that generates compound ideas and identifies those that are most likely to achieve a drug discovery project’s objectives. The compound ideas should be synthetically feasible; to achieve this, new structures are generated from an initial compound using medicinal chemistry ‘rules’. These are then scored against a profile of property criteria using a probabilistic scoring method and visualized in ‘chemical space’ to allow many ideas to be rapidly explored and prioritized for detailed consideration.

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Guided Application of Med Chem Rules to Generate ‘Good’ Ideas

Friday, 29 April 2011 00:00
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Matt Segall

Matt gave this presentation at the ACS Spring meeting 2011 in Anaheim.

Abstract

Computational tools can guide the selection of high quality compounds, with a good balance across multiple properties, and guide strategies to design improved compounds. But, can software propose ideas for better compounds? We will demonstrate an approach that generates compound ideas and identifies those that are most likely to achieve a drug discovery project’s objectives. The compound ideas should be synthetically feasible; to achieve this, new structures are generated from an initial compound using medicinal chemistry ‘rules’, using a method similar to [1]. These are then scored against a profile of property criteria using a probabilistic scoring method [2] and visualized in ‘chemical space’ to allow many ideas to be rapidly explored and prioritized for detailed consideration.

[1] Stewart et al. Bioorg. Med. Chem (2006), 14 p. 7011

[2] Segall et al. Chem. & Biodiv (2009), 6 p. 2144

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A New Generation of Possibilities

Monday, 07 March 2011 00:00
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Matt Segall

This whitepaper describing Nova's technology has not yet been published.

Abstract

Using in silico predictive models and multi-parameter optimisation techniques allows large numbers of compounds to be quickly assessed with respect to a profile of properties required for a successful compound in a drug discovery project. With these predictive methods, it becomes possible to consider a large number of ideas for potential compounds that can be easily created and entered into a computer by an individual. In this article we describe a method that automatically generates chemically relevant compound ideas from an initial molecule, based on medicinal chemistry ‘transformation rules’ taken from examples in the literature. These are then prioritised using in silico models and a probabilistic scoring algorithm to identify the compound ideas most likely to satisfy a user-defined profile of required properties. Embedded in an intuitive, visual user interface, this approach provides a powerful means to explore potential chemistry to identify high quality leads or to improve properties in lead optimisation. We demonstrate that the set of 206 transformations employed is generally applicable, produces a wide range of new compounds and is representative of the types of modifications previously made to move from lead-like to drug-like compounds. Furthermore, we show that more than 94% of the compounds generated by transformation of typical drug-like molecules are acceptable to experienced medicinal chemists. Finally, we illustrate an application of our approach to the lead that ultimately led to the discovery of Duloxetine, a marketed serotonin reuptake inhibitor. Our analysis results in the identification of a diverse range of high scoring compounds, including Duloxetine itself.

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Poster: Guiding Focused Design of Potent Leads with Improved Metabolic Stability

Thursday, 07 October 2010 00:00
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Matt Segall

Matt presented this poster at ISSX in September 2010.

Abstract:

A number of methods have been developed for the prediction of regioselectivity of metabolism by the major drug metabolising isoforms of Cytochrome P450 [1,2,3]. However, while valuable, predicting the relative proportion of metabolite formation at different sites on a molecule is only a partial solution to designing more stable molecules. Valuable additional information comes from predicting a measure of the vulnerability of each site to metabolism. Such a measurement is the site lability, as calculated by StarDrop. This important factor in determining the overall rate of metabolism, when combined with other descriptors relating to substrate affinity, can provide good predictive models of in vitro metabolic rate which can, in turn, guide design of compounds with improved stability.

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Exploring Project Spaces to Quickly Identify High Quality Compounds

Monday, 27 September 2010 00:00
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Ed Champness

In this presentation Ed Champness demonstrates some of StarDrop's features for identifying and selecting compounds, using chemical space representations to illustrate two case studies.

Abstract:

The term chemical space is generally understood to describe the universe of possible chemistries that may exist. With this number being almost incomprehensible, the term is more often used in a specific company or project context where the bounds are more manageable both theoretically and computationally. In this paper we present a method for constructing and visualising chemical space for use in a project setting. When exploring this space, we need to consider how to balance quality and diversity, or alternatively, ‘exploration’ and ‘exploitation’, and how to avoid introducing bias into our selections. It is often important not to focus too quickly, but instead to gather data on potential backup series in order to mitigate risk and understand the SAR. With this in mind, basing our selections purely upon a single property, such as the potency of the compounds, can lead us towards very different choices that may ultimately result in more complex challenges as we attempt to design leads which have a good overall balance of properties. We present examples and case studies to illustrate these concepts and results from both Hit-to-Lead and Lead Optimisation.

Ed gave this presentation at MipTec, September 2010, in Basel.

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Beyond Profiling: Using ADMET Models to Guide Decisions

Tuesday, 01 December 2009 17:52
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Matt Segall

This article was published in Chemistry & Biodiversity, Volume 6, Issue 11, Pages 2144 - 2151

Abstract

ADMET models, whether in silico or in vitro, are commonly used to ‘profile’ molecules, to identify potential liabilities or filter out molecules expected to have undesirable properties. While useful, this is the most basic application of such models. Here we will demonstrate how models may be used to go ‘beyond profiling’ to guide key decisions in drug discovery. For example, selection of chemical series to focus resources with confidence or design of improved molecules targeting structural modifications to improve key properties....

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Predictive ADME Models in Drug Discovery: Can You Trust Them? Can You Afford Not To?

Tuesday, 13 October 2009 15:07
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Ed Champness

In this presentation, Alan Beresford discusses drug discovery and considers how ADME models fit into the process as a step necessary for helping to manage the numbers game. Alan describes how, by using appropriate interpretation of ADME model results, it is possible to credibly include them within a traditional screening cascade.

This presentation was given at the MedChem USA Conference in 2007.

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Poster: Application of in Silico (ADMEnsa Interactive) and ADME/PK Assays in the Identification of New Chemical Entities (NCEs) for Pre-Clinical Evaluation

Tuesday, 13 October 2009 14:56
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Ed Champness

The current paradigm of  drug discovery utilising chemical library synthesis coupled with high throughput screening technologies often gives rise to a situation whereby drug discovery programmes are compound rich although poor in ADME properties. As such, the ADME properties of compounds require optimisation, through the phases of lead optimisation, prior to progression for clinical development increasing the cost and duration of the process. A prime driver of drug discovery is therefore the early identification of compounds from diverse chemical spaces with optimal ADME properties.

This poster was displayed at the ISSX international meeting in Japan, 2007.

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Poster: Guiding the Decision-Making Process to Identify High Quality Compounds

Tuesday, 13 October 2009 14:56
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Matt Segall

Defining a property profile is subjective and often leads to lengthy, interdisciplinary discussions about the criteria and their relevance. For example, is it worth sacrificing some potency to gain additional metabolic stability or solubility? However, a question that is rarely asked is, “What impact would that trade-off have on the final outcome?”, particularly given the underlying uncertainty.

This poster was displayed at the ISSX North American meeting in Baltimore, USA, 2009.

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Improving Drug Discovery Efficiency via In Silico Calculation of Properties

Tuesday, 13 October 2009 14:01
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Dan Ortwine

Dan Ortwine gave this presentation as part of a workshop at the ISSX North American meeting in Baltimore, MD, USA, 2009.

The workshop, titled Effectively Using In Vitro Data, In Silico Models and Data Mining at Early Stage Drug Development had the following introduction:

At the early stages of drug discovery there are large numbers of compounds synthesized to examine the chemical landscape needed for discovering the lead compounds. Chemical diversity plays a key role at this stage to increase the probability of success when narrowing down to clinical candidates. In this workshop, we will discuss a number of currently available in vitro assays and in silico tools and how they are complementary. We hope by the end of the workshop to provide a platform for decision making purposes in early drug discovery.

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