The three ideal data requirements for an effective FEP calculation can be characterized as:

• The availability of a high-quality crystal structure with a relevant ligand
• The quantity and quality of a dataset of ligands for validation
• The generation of an appropriate ligand set for prediction

Here, we describe these requirements and best practices for FEP calculations in detail.

Availability of a high-quality crystal structure

To effectively implement FEP, it is ideal to have access to a high-quality crystal structure with a known ligand, relevant to the ligands in your data set, bound along with extended knowledge of the binding mode interactions. What is crucial, in all relative FEP calculations, is that the binding mode of the ligand is unambiguously defined within the protein structure. While a crystal structure to a resolution below 2.2 Å is ideal, ligand occupancy, unresolved sidechains, and overall quality of the structure must also be evaluated.

Figure 1. Example of protein structures with good (green surface) and poor (red surface) electron density maps for the same target. (A) Protoporphyrin ligand from myoglobin, 1A6M PDB ID (1.0 Å resolution). (B) Protoporphyrin ligand from hemoglobin, 1S0H PDB ID (3.0 Å resolution). Image generated with Flare™ v7.

Like all structure-based techniques, the binding mode plays a crucial role in FEP calculations when predicting the activity of a set of ligands. The binding mode of the known ligand serves as a reference, to which the novel ligand binding modes and poses are compared.

Examining electron density to observe a well resolved and known binding mode and pose can be helpful in choosing input for your FEP calculation. The example provided in Figure 1 serves as an illustration of this analysis and how, in such an example, choosing PDB 1A6M (Figure 1A) is likely the better choice in terms of picking a ligand binding event you have confidence in.

Adequacy of ligand data for validation

FEP, as with other computational methods, requires validation, and this relies on a dataset of experimentally measured ligand data for the system under investigation. This validation stage, otherwise known as ‘benchmark’ mode, is an initial test of FEP to determine how well the predictions match experimental data. If there is a good match, this gives confidence in the FEP set up and model, such that it is worthwhile moving into the ‘production’ mode of making predictions based on no experimental measurements. Appropriate ligand data used in benchmarking are typically derived from the following types of experimental data:

• XC₅₀ (half-maximal activity concentration),
• K_i (inhibition constant), or
• K_d (dissociation constant).

Critical evaluation of the diversity and reliability of the ligand dataset is central to the validation of the FEP calculation i.e., the quality of the benchmark. It is preferable to have a ligand set with a wide range of binding affinities, including both weaker and stronger binders, to capture a broader spectrum of ligand-protein interactions, and to validate the FEP predictions fully. An accuracy of < ± 1 kcal/mol of predicted affinity to the experiment can be achieved in FEP, and this will be established in the benchmark validation. Note, in the analysis of the benchmark study, it is worth taking into account how reliable the experimental data is, and again ideally, where possible it is preferable to use single source assay information to avoid large uncertainty in the experimental results that can arise due to variation in conditions.

Presence of appropriate ligands for prediction

Based largely on the results of the benchmark study the FEP project can move into a production mode: the model is ready to make predictions based on new molecular designs. As in the benchmarking, the diversity and suitability of the ligands for FEP calculations must be evaluated for the production calculation. Testing a set of new ligands which are very structurally dissimilar will not work well in relative FEP given the assumption stated above.

The structural similarities between the reference ligand and the predicted ligands must be assessed to establish whether they share key structural features, common substructures, and consistent binding mode. Suitable similarity ensures that the FEP calculations provide meaningful and reliable predictions for the ligands of interest. An illustration of such an analysis is provided in Figure 2.

Figure 2. Examples of permitted alchemical changes within an FEP calculation. Image generated with Flare.

Figure 2 illustrates an FEP graph which links up ligands by various transformations. Each link has a ‘score’ which ranges from 0 (no similarity) to 1 (identical) and so gives an indication of the ease of the perturbation; a higher link score (typically > 0.4) indicates a link (transformation) more likely to calculate successfully. In terms of setting up an FEP calculation for production mode and making successful predictions for a new ligand data set, it is important to check you have a network of links with good (> 0.4) link scores. Very low link scores represent a data set with too much structural diversity. Cresset Discovery scientists have the expertise and experience to guide you in creating such a dataset.

Determining if FEP is suitable for your project

The feasibility of implementing FEP calculations for your project depends on several key factors. The availability of a high-quality crystal structure with a relevant ligand, a well-defined binding mode, and adequate ligand data for validation can be critical elements for accurate and reliable FEP calculations. However, success with FEP can still be achieved even in the absence of certain information.

The presence of appropriate ligands for prediction, with structural similarities to the reference ligand and a range of binding affinities, enhances the validity and applicability of FEP predictions. When these ligand requirements are met the amenability of your target to FEP generally can be initially assessed with a benchmark study. Careful evaluation and consideration of the benchmark run will guide the decision on whether FEP can be effectively utilized to support your project’s objectives, in a production run (or predictive mode) or if alternative approaches should be explored.

Alternative methods, such as ligand-based approaches, bioisosteric replacement, and de novo design, can be considered in cases where the requirements for FEP are not fully met. Expertise at Cresset can help you evaluate the suitability of your project for FEP.

Successful application of FEP using Cresset methodology is demonstrated in a benchmarking study which accurately calculated binding affinities for a dataset of 30 ligands that bind between the lipid and GPCR interface in P2Y1. In this study, predicted binding affinities agree with experimental measurements and are in line, or better than data published in the literature. On the basis of this, the FEP model can be taken forward with confidence and used to test and predict new designs.

Cresset Discovery’s experienced team is committed to providing the most appropriate method for developing your project. Contact us for a confidential discussion about your goals and we can advise on whether FEP would be suitable for your project or if other, more appropriate computational methods would be suitable to progress your project.

The post Are Free Energy Perturbation (FEP) methods suitable for your project? appeared first on MassBio.

The globalized outsourcing model of modern drug discovery is driving efficiencies across the Design-Make-Test-Analyze (DMTA) cycle. With projects typically involving multiple specialist CROs, effective communication is key to ensuring the success of these collaborations.

In this article, we outline some of the challenges in sharing chemistry data and demonstrate how Cresset Discovery collaborates seamlessly with customers and external partners using the Torx® cloud-based platform. The integration of Torx within Cresset’s CADD software streamlines sharing of project data and facilitates secure communication of information with customers and other external partners (Figure 1).

Figure 1: Possible interaction routes between Cresset Discovery, our customer, and other stakeholders in a drug discovery project using Torx

Challenges

Effective communication is central to efficiently progressing around the DMTA cycle. From hypotheses and molecular designs, followed by synthesis, biological testing and data analysis, productivity of a project team depends on efficient data sharing between medicinal chemistry/computational chemistry/CRO partners. Unfortunately, this can be a tedious and time-consuming task when data and chemistry designs are stored in a range of file formats. Sharing hypothesis within a team requires the preparation of reports/presentations that must then be uploaded to shared drives. The transfer of chemical data can often lead to loss, or even erroneous transfer, resulting in duplication of work when structures or data must be re-entered manually. From a scientific perspective, compound ideas are typically represented in 2D and taken forward for synthesis on the assumption that the molecule fits the 2D pharmacophore. However, this doesn’t make biological sense when viewed as a 3D conformation and in the context of the protein binding site. This wastes synthetic and computational chemistry, as well as biological testing resources.

How Cresset Discovery uses Torx to streamline data transfer for discovery projects

Cresset Discovery uses Torx, a web-based, molecule aware, DMTA platform to communicate with our customers.¹  The platform centralizes all project data, connecting discovery teams and partners securely, so that the latest information is always accessible, in the right format at the right time. All project compounds, syntheses, hypotheses, data and challenges are captured and tracked in a secure and fluid fashion, avoiding duplication of work between colleagues and partners.

Cresset Discovery utilizes a seamless integration between the ligand and structure-based drug design solution Flare² and Torx to efficiently retrieve customer data and carry out further computationally enhanced 3D-modeling. The analyses are then shared in real time with customers and other external partners who may then engage with their third parties for further synthesis work. Using the Torx platform eliminates duplication of work through data sharing and the need to generate slides for project updates, or to track team resources.

An example of a Cresset Discovery workflow used is shown in Figure 2 for a Cyclin-Dependent Kinase 9 (CDK9) inhibitors case study carried out by our team to demonstrate the efficiency of the application.^3,4

When a customer uses Torx to share SAR compounds and design ideas with Cresset Discovery, these can be fetched seamlessly into Flare using the Torx API and a dedicated extension to Flare. In this example (Figure 2), an automated Hit Expansion of the lead compound was enumerated, to generate close analogue ideas. The candidates were then triaged by running Flare FEP binding predictions. The results of the binding predictions were sent back in real time to Torx, enabling the project team to make better, informed decisions and prioritize the best molecules for synthesis. There was no need for multiple uploads and download of structures and unnecessary presentation slides.

Figure 2: An example of a Cresset Discovery workflow using Torx with stakeholders

Conclusion

When partnering with Cresset Discovery CRO, customers benefit from secure, real-time sharing of project data and communications utilizing the seamless integration of Torx within molecular modeling solution Flare. The enhanced CADD analyses and models are shared in real time, leading to models being adopted by multiple stakeholders, improving the design process and reaching your next project milestone faster.

Engage Cresset Discovery to accelerate your project

Cresset Discovery serves as a true partner for computational drug discovery. Our expert team has a breadth of experience across medicinal and computational chemistry and biology, and works alongside you to deliver your project goals. Whether you are looking to validate early-stage targets, find new hits or optimize them to deliver a clinical candidate, the team’s deep technical expertise spans from ligand, fragments, and structure-based drug discovery capabilities (SBDD) across a wide range of therapeutic targets. Partnering with us, you will benefit from the robust combination of our experienced scientists applying the right technology at the right time, to accelerate your project cost-effectively.

Watch our webinars to learn more about Streamlining collaboration and information delivery of outsourced discovery projects and Streamlining CADD and med chem communications.

To learn more about how we can support your project, request a no-risk confidential discussion with our team. If you’d like to explore the possibility of using our software tools on your own project, then you can also request a free CADD software evaluation, or Torx demonstration.

References

1. Torx®, Torx Software®, Litlington, Cambridgeshire, UK; https://www.torx-software.com
2. Flare™, Cresset®, Litlington, Cambridgeshire, UK; https://www.cresset-group.com/flare/; Cheeseright T., Mackey M., Rose S., Vinter, A.; Molecular Field Extrema as Descriptors of Biological Activity: Definition and Validation J. Chem. Inf. Model. 2006, 46 (2), 665-676; Bauer M. R., Mackey M. D.; Electrostatic Complementarity as a Fast and Effective Tool to Optimize Binding and Selectivity of Protein–Ligand Complexes J. Med. Chem. 2019, 62, 6, 3036-3050; Maximilian Kuhn, Stuart Firth-Clark, Paolo Tosco, Antonia S. J. S. Mey, Mark Mackey and Julien Michel Assessment of Binding Affinity via Alchemical Free-Energy Calculations J. Chem. Inf. Model. 2020, 60, 6, 3120–3130
3. Rapid approaches to new scaffold generation – accelerate hit-to-lead
4. Prioritizing the Most Promising Virtual Screening Hits

The post Streamline sharing of CADD data seamlessly to accelerate your drug design process appeared first on MassBio.

Enter the augmented worker with AR/VR headset…. Read the full article published in Lab Manager here. 

Interested in learning more about Full Spectrum Lab Services from CBRE? Feel free to reach out to caitlin.cricco@cbre.com at any time!

The post Expedite Training, Technical Support, and Lab Design by Leveraging Mixed Reality appeared first on MassBio.

This webinar explores the challenges associated with site versus central image reading in oncology clinical trials, how they impact trial results, and their root for better drug efficacy evaluation. 

This webinar covers the following topics: 

• Understanding the impact of site vs. central read discrepancies on imaging outcomes and drug efficacy evaluation 
• Identifying the factors that contribute to site vs. central read discrepancies, including real-world examples 
• Discussing ways to address and overcome these discrepancies 
• This webinar is intended for clinical trial sponsors, imaging experts, scientists, and anyone involved in designing and conducting imaging clinical trials. 

Don’t miss this opportunity to learn more about site versus central read variability and how to mitigate its impact on clinical trial results. Watch the replay here!

Our presenter panel includes high level experts:  

• Dr Cinzia Dello Russo – Researcher in Pharmacology, Department of Healthcare Surveillance and Bioethics, Section of Pharmacology, Università Cattolica del S. Cuore, Rome, Italy 
• Dr Navarra Pierluigi – Head, Chair of Pharmacology, Dept. of Healthcare Surveillance and Bioethics, Section of Pharmacology Università Cattolica del S. Cuore, Rome, Italy 
• Dr Antoine Iannessi – Senior Medical Director, Median Technologies 

The post Closing the Gap: Strategies for Resolving Imaging Discrepancies in Oncology Drug Trials appeared first on MassBio.

Link:  Clinical trial patient recruitment challenges and solutions

Title: Clinical trial patient recruitment challenges and solutions

Introduction: Did you know that globally, around 80% of clinical trials fail to enroll participants on time?

In our most recent blog post we’re covering some of the top challenges impeding patient recruitment, along with solutions to overcome these challenges. 

The post Blog Post | Clinical trial patient recruitment challenges and solutions appeared first on MassBio.

The post Leveraging Technology to Accelerate Biopharma Commercialization Planning appeared first on MassBio.

The client was using a legacy LIMS that could not electronically track and store all data from cannabis samples. These gaps in functionality were forcing the company to maintain paper records and causing inefficiencies in the sample processing workflow.

To resolve these issues, GraVoc developed a custom cloud LIMS that streamlines sample processing, allowing the client to track samples, import test results into the application, and automate analysis of results against state regulations.

Now, with all sample data digitally captured and centralized in the new LIMS application, the client can eliminate paper-based processes, improve data visibility, and ensure accuracy of records.

Click here to read the full case study.

The post GraVoc Designs Custom LIMS Cloud Application to Eliminate Paper-Based Processes for Cannabis Testing Lab appeared first on MassBio.