Periodic Reporting for period 1 - PACE (Platform for Rapid Development of Personalized Nanomedicine Drug Delivery Systems)
Reporting period: 2020-09-01 to 2022-02-28
The Problem
In the FET project, EVO-NANO, the goal is to create an integrated platform for the artificial evolution and validation of novel drug delivery systems (DDS) for cancer treatment using nanoparticles (NP). This platform is intended to speed up the screening of DDS candidates by providing a full pipeline for rapid development of effective NP-based cancer therapies that are safe, and have adequate biodistribution and delivery characteristics. The platform is based on integrating AI (artificial intelligence) engine with tumour simulators at different scales. AI engine enables high-throughput generation and testing of NP-treatments which are tested in the tumour-simulation part of the platform.
The problem we are tackling in PACE project is how to successfully bring this technology to market.
Importance:
The development of EVO-NANO technology into the full-fledged product will offer the following advantages:
● Rapid selection of drugs to use to functionalize NPs for effective drug delivery into tumors;
● Prediction of the extravasation ability of functionalized NPs;
● Prediction of the effect of functionalized NPs on patient-specific tumour;
● In silico testing of various functionalization strategies and NP compositions to determine delivery efficacy and dosage;
● Identification of new and more precise treatment options leading to refining study designs and speed up of the completion times for various preclinical studies;
The cumulative effect of these improvements will be that chosen DDS will have a greater likelihood of successfully demonstrating effectiveness and safety. This will lead to increased R&D efficiency of preclinical studies since most of the new drug development costs are related to the costs of failed projects
Overall objectives:
The overall objective is to develop a viable business model for EVO-NANO platform by designing and exploring multiple alternatives while closely listening to customers and iteratively adapting our early assumptions until we find the optimal model to scale.
Most importantly, in the last phase of the PACE project, we realized that we need to pivot away from nanomedicine-focused, towards a more general, drug-focused predictive tool. As a result of that we created the appropriate Business plan and established strategic cooperation with the company which organizes clinical trials and has clinical data necessary for EVO-NANO platform validation.
In the FET project, EVO-NANO, the goal is to create an integrated platform for the artificial evolution and validation of novel drug delivery systems (DDS) for cancer treatment using nanoparticles (NP). This platform is intended to speed up the screening of DDS candidates by providing a full pipeline for rapid development of effective NP-based cancer therapies that are safe, and have adequate biodistribution and delivery characteristics. The platform is based on integrating AI (artificial intelligence) engine with tumour simulators at different scales. AI engine enables high-throughput generation and testing of NP-treatments which are tested in the tumour-simulation part of the platform.
The problem we are tackling in PACE project is how to successfully bring this technology to market.
Importance:
The development of EVO-NANO technology into the full-fledged product will offer the following advantages:
● Rapid selection of drugs to use to functionalize NPs for effective drug delivery into tumors;
● Prediction of the extravasation ability of functionalized NPs;
● Prediction of the effect of functionalized NPs on patient-specific tumour;
● In silico testing of various functionalization strategies and NP compositions to determine delivery efficacy and dosage;
● Identification of new and more precise treatment options leading to refining study designs and speed up of the completion times for various preclinical studies;
The cumulative effect of these improvements will be that chosen DDS will have a greater likelihood of successfully demonstrating effectiveness and safety. This will lead to increased R&D efficiency of preclinical studies since most of the new drug development costs are related to the costs of failed projects
Overall objectives:
The overall objective is to develop a viable business model for EVO-NANO platform by designing and exploring multiple alternatives while closely listening to customers and iteratively adapting our early assumptions until we find the optimal model to scale.
Most importantly, in the last phase of the PACE project, we realized that we need to pivot away from nanomedicine-focused, towards a more general, drug-focused predictive tool. As a result of that we created the appropriate Business plan and established strategic cooperation with the company which organizes clinical trials and has clinical data necessary for EVO-NANO platform validation.
In this project UNSPF collected and analyzed data on state-of-the-art and use of AI in personalized nanomedicine, EVO-NANO platform performance requirements, regulatory requirements and potential customers and partners. At the same time MADISI collected and analyzed data related to commercialization, commercial due diligence, valuation and market strategy.
These two partners analyzed preliminary positioning and jointly formulated range of marketable applications, IP strategy and market strategy. Based on that analysis UNSPF defined which updates and modifications should be performed on EVO-NANO platform so it can be better tailored towards market application.
The work was carried in several phases.
In the first phase we performed Initial IP Position and Strength analysis and reported results in D1.1. In short we analyzed IP strengths and risks of the EVO-NANO platform and performed IP review through analysis of publications and patent trends for various metrices. Finally we defined IP as “A computational program that predicts how nano-therapies will interact with cancerous tumors”, while the market for this platform will fall within AI (Artificial Intelligence) and cancer treatment prognosis.
In the second phase we performed Macro Market analysis (reported in D1.2). There we analyzed how the EVO-NANO platform can be positioned on market and what methodology should be used. After that, we gave an overview of Regulatory procedures, in both the USA and EU and analyzed Nano-medicine market (both in total and for cancer treatment), its ongoing trends, most prominent companies and FDA-approved nano-drugs. We also analyzed AI medicine marked where we present the overall trend, market segmentation and cmpanies involved in AI therapies. We concluded that there is a growing niche for the innovation contained in this project.
Third phase focused on Application Analysis (reported in D1.3). There we analyzed specific application performance requirements and regulations, specific costs and price points, and know potential customers/partners. We performed interviews with representatives of related companies and the main conclusion is that this is a niche & early stage market segment with very long development time and associated high investment cost.
Finally, we outlined a market strategy (reported in D1.4). Main points are:
• Because private investors have been found to be reluctant, it can therefore be determined that the best method forward will be further grants;
• The need for this technology has been reaffirmed;
• At this moment, the EVO-NANO technology lacks the necessary functionality and tangibility to be patent protected;
• Therefore, in the future, planning should include the product as a service unless the technology has been absorbed into upstream activities, including a fully developed AI platform that has shown proven efficacy in predicting outcomes in drug development.
These two partners analyzed preliminary positioning and jointly formulated range of marketable applications, IP strategy and market strategy. Based on that analysis UNSPF defined which updates and modifications should be performed on EVO-NANO platform so it can be better tailored towards market application.
The work was carried in several phases.
In the first phase we performed Initial IP Position and Strength analysis and reported results in D1.1. In short we analyzed IP strengths and risks of the EVO-NANO platform and performed IP review through analysis of publications and patent trends for various metrices. Finally we defined IP as “A computational program that predicts how nano-therapies will interact with cancerous tumors”, while the market for this platform will fall within AI (Artificial Intelligence) and cancer treatment prognosis.
In the second phase we performed Macro Market analysis (reported in D1.2). There we analyzed how the EVO-NANO platform can be positioned on market and what methodology should be used. After that, we gave an overview of Regulatory procedures, in both the USA and EU and analyzed Nano-medicine market (both in total and for cancer treatment), its ongoing trends, most prominent companies and FDA-approved nano-drugs. We also analyzed AI medicine marked where we present the overall trend, market segmentation and cmpanies involved in AI therapies. We concluded that there is a growing niche for the innovation contained in this project.
Third phase focused on Application Analysis (reported in D1.3). There we analyzed specific application performance requirements and regulations, specific costs and price points, and know potential customers/partners. We performed interviews with representatives of related companies and the main conclusion is that this is a niche & early stage market segment with very long development time and associated high investment cost.
Finally, we outlined a market strategy (reported in D1.4). Main points are:
• Because private investors have been found to be reluctant, it can therefore be determined that the best method forward will be further grants;
• The need for this technology has been reaffirmed;
• At this moment, the EVO-NANO technology lacks the necessary functionality and tangibility to be patent protected;
• Therefore, in the future, planning should include the product as a service unless the technology has been absorbed into upstream activities, including a fully developed AI platform that has shown proven efficacy in predicting outcomes in drug development.
The main progress we made is the creation of the basis for further investment into EVO-NANO technology. We created the business plan, mapped the roadmap for further development and made several important contacts with companies and hospitals that can provide clinical data necessary for validation of the technology. Overall this project prepared the commercial foundation for furthering the EVO-NANO technology in the market. Through interviews and secondary market research we found market opportunities and threats. These have been analyzed and a preliminary market strategy has been developed.
Potential impact that EVO-NANO platform could have is large. One of the biggest problems in developing new cancer treatments is the costly failure of clinical trials. In oncology, 97% of trials fail. The primary source of trial failure has been and remains an inability to demonstrate treatment efficacy. It accounts for more than half of failed trials. The observed efficacy is greatly influenced by both: the properties of a drug and the characteristics of a patient. For instance, it has been shown that in cases when biomarkers are used to select patients, the probability of success is almost 7 times higher compared to trials that do not use them.
At the same time, the number of oncology clinical trials is on the rise, with 2300 trials that started globally in 2018. Average number of patients per oncology trial is 311. Research showed that 85% of clinical trials are delayed, mostly due to problems in finding suitable patients. It was also estimated that each day of delay in running a clinical trial translates to a $600.000 – $8M loss for the company.
In summary, we are targeting a rising market of oncology clinical trials, where each trial has a very high probability of failure, and each failure costs several million dollars.
Once we reach the market application, our goal is to de-risk clinical trial execution, by identifying the most responsive patient cohort, through the use of our software during clinical protocol development.
Potential impact that EVO-NANO platform could have is large. One of the biggest problems in developing new cancer treatments is the costly failure of clinical trials. In oncology, 97% of trials fail. The primary source of trial failure has been and remains an inability to demonstrate treatment efficacy. It accounts for more than half of failed trials. The observed efficacy is greatly influenced by both: the properties of a drug and the characteristics of a patient. For instance, it has been shown that in cases when biomarkers are used to select patients, the probability of success is almost 7 times higher compared to trials that do not use them.
At the same time, the number of oncology clinical trials is on the rise, with 2300 trials that started globally in 2018. Average number of patients per oncology trial is 311. Research showed that 85% of clinical trials are delayed, mostly due to problems in finding suitable patients. It was also estimated that each day of delay in running a clinical trial translates to a $600.000 – $8M loss for the company.
In summary, we are targeting a rising market of oncology clinical trials, where each trial has a very high probability of failure, and each failure costs several million dollars.
Once we reach the market application, our goal is to de-risk clinical trial execution, by identifying the most responsive patient cohort, through the use of our software during clinical protocol development.