AstraZeneca invites partners across the global scientific community to contribute established screening assays, or predictive models relevant to oral bioavailability and/or cell permeability of macrocyclic peptides that can help map and model the property space required for oral and cell-permeable macrocyclic peptides. They particularly welcome: 
<ul>
<li>High-throughput screening assays (in vitro and ex vivo) or advanced in silico platforms for quantifying key properties relevant to oral bioavailability and/or cellular permeability of macrocyclic peptides</li>
<li>Novel or proprietary methods for predicting, quantifying, or modelling the physicochemical space that governs permeability and oral absorption in macrocyclic peptides</li>
<li>Annotated data generated from these screening methods</li>
</ul>
Proposed collaborations or data contributions should either provide experimental proof-of-concept, published evidence, or actionable approaches for generating datasets or screening tools that address these key parameters. Solutions that enable parallel evaluation of multiple macrocyclic peptides, or which deliver mechanistic or predictive insights into the determinants of oral and cell permeability, are particularly encouraged. Please see page [NUMBER] of the submission form [LINK] for further details on the solution criteria.
The selected collaborator will receive at least 8 benchmark compounds to confirm permeability ranking in a blinded study. Following screening, compounds will be unblinded for the collaborator and available to use freely. 
Out of scope: Proposals that are hypothetical, lack experimental feasibility, or involve low-throughput experimental or predictive modelling approaches that are not scalable for parallel evaluation of large compound sets will be given a lower priority. 
Submission Information
AstraZeneca invites applications from both academic and biotech organizations, and encourage proposals based on existing technologies or methods. Applicants should complete the submission form [ADD LINK] which should contain a brief, non‑confidential overview of your proposal, demonstrating how the RFP requirements are satisfied by your approach. Proposals should outline the resources required to solve the problem. To submit your proposal, please visit our <a href="" title="http://discover.in-part.com/">website</a>, register, and submit your application form under the appropriate Connect campaign. 
Opportunity for Collaboration
<ul>
<li>Co-development opportunity with AstraZeneca </li>
<li>Technology validation support: AstraZeneca’s expertise in assay standardization and FAIR data principles to help optimize and validate your screening methods </li>
<li>Co-publication opportunities: joint publication rights for significant findings and model developments arising </li>
<li>Global network access: Introduction to AstraZeneca’s broader peptide research network and potential additional collaboration opportunities</li>
</ul>

High Throughput Screening Assays to Map Macrocyclic Peptide Permeability and Stability Properties

AstraZeneca aims to build a world class peptide discovery platform, combining **phage and mRNA display****technologies and amenable to the incorporation of a wide variety of non-canonical amino acids** (ncAAs) in those display systems, from ncAAs capable of forming covalent bonds to enable new chemistries in peptides, such as cyclization, to ncAAs which can introduce novel physicochemical properties, such as altered hydrophobicity, charge, or backbone rigidity, to manipulate peptide solubility, cellular uptake, or overall structural dynamics.
The team at AstraZeneca is seeking solutions for the efficient incorporation of ncAAs in phage and/or mRNA display systems that allow for thioether macrocycle cyclisation and/or efficient incorporation of a range of amino acids. Proposed solutions to this challenge should meet the following solution requirements to be considered for funding by AstraZeneca. The selected collaborator will need to provide AstraZeneca with reagents allowing for at least one of the following: 
<ul>
<li>Efficient incorporation of ncAAs allowing for thioether macrocycle cyclisation in a phage display system. This should go beyond the simple use of evolved tRNA synthetases and overcome some of the limitations of stop codon reprogramming</li>
<li>Efficient incorporation of ncAAs allowing for thioether macrocycle cyclisation in an mRNA display/cell free protein expression system</li>
<li>Incorporation of other types of ncAA such as but not limited to N-methylated amino acids, fluorosulphate-amino acid, Aib, in an mRNA display/cell free protein expression system</li>
</ul>
Submission InformationAstraZeneca invites applications from both academic and biotech organizations. Applicants should complete the submission form [ADD LINK] which should contain a brief, non‑confidential overview of your proposal, demonstrating how the RFP requirements are satisfied by your approach. Proposals should outline the resources required to solve the problem. Additional submission requirements are highlighted on page [NUMBER] of the submission form. To submit your proposal, please visit our <a href="" title="http://discover.in-part.com/?_gl=1*ug8na4*_ga*ODUzNTU5NDQ5LjE3NjE1NTc2Njk.*_ga_MV1ZERQX2Q*czE3NjQ3Nzk3NzgkbzExOSRnMSR0MTc2NDc3OTc4OSRqNDkkbDAkaDA.">website</a>, register, and submit your application form under the appropriate Connect campaign.

Non-canonical Amino Acids for in vitro Display Systems

AstraZeneca is seeking datasets or physical/virtual libraries of macrocyclic peptides that can help map and model the property space required for oral and cell permeable macrocyclic peptides. They particularly welcome: 
<ul>
<li>Annotated in vitro/vivo datasets describing permeability, oral bioavailability, enzymatic stability or other relevant properties of peptide macrocycles—including both proprietary and published collections</li>
<li>Libraries of macrocyclic peptides which are suitable for screening, ideally with reference activity which would enable AI-led property mapping and predictive modelling. Physical compound sets are preferred but virtual libraries may be accepted for use once training datasets have been established</li>
<li>Novel or proprietary methods for predicting, quantifying, or modelling the physicochemical space that governs permeability and oral absorption in macrocyclic peptides</li>
</ul>
Submissions should either provide experimental proof-of-concept, published evidence, or actionable approaches for generating datasets or screening tools that address these key parameters. Solutions that enable parallel evaluation of multiple macrocyclic peptides, or which deliver mechanistic or predictive insights into the determinants of oral and cell permeability, are particularly encouraged. Please see page [NUMBER] of the submission form [LINK] for further details on the solution criteria.
Out of scope: Proposals that are hypothetical, lack experimental feasibility, or involve low-throughput experimental or predictive modelling approaches that are not scalable for parallel evaluation of large compound sets will be given a lower priority.
Submission Information
AstraZeneca invites applications from both academic and biotech organizations. Applicants should complete the submission form [LINK] which should contain a brief, non‑confidential overview of your proposal, demonstrating how the RFP requirements are satisfied by your approach. Proposals should outline the resources required to solve the problem. To submit your proposal, please visit our <a href="" title="http://discover.in-part.com/?_gl=1*ug8na4*_ga*ODUzNTU5NDQ5LjE3NjE1NTc2Njk.*_ga_MV1ZERQX2Q*czE3NjQ3Nzk3NzgkbzExOSRnMSR0MTc2NDc3OTc4OSRqNDkkbDAkaDA.">website</a>, register, and submit your application form under the appropriate Connect campaign.
Opportunity for Collaboration
<ul>
<li>Co-development opportunity with AstraZeneca </li>
<li>Access to comprehensive screening data: contributors will receive complete physicochemical, permeability, and stability profiles for all compounds screened through AstraZeneca’s validated cascade </li>
<li>AI/ML model insights: collaborators gain access to predictive models and property-activity relationships derived from their contributed data, enhancing their own research capabilities </li>
<li>Co-publication opportunities: joint publication rights for significant findings and model developments arising from collaborative datasets </li>
<li>Expanded dataset value: data becomes part of a larger, more statistically robust dataset, increasing its scientific impact and citation potential </li>
</ul>

AI/ML-ready Datasets for Peptide Permeability & Oral Bioavailability or Macrocyclic Peptide Screening Libraries to Enable Oral, Cell-permeable Macrocycle Design

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