DeepMatcher® Success Stories
Successful Collaboration Between Syntekabio and QIMRB in Targeting Lung Diseases
Syntekabio and QIMRB, an Australian research institution, have embarked on a collaboration that successfully identified a promising lead candidate for treating chronic obstructive pulmonary disease (COPD) and lung cancer. Leveraging Syntekabio's AI-driven drug discovery platform, the collaboration identified a molecule with significant efficacy within just 5 months, and the company is preparing for future clinical trials.
Rapid Identification of Promising Candidates: Using Syntekabio’s advanced AI-based platform, the collaboration swiftly identified top hits, including two Demethylase inhibitors, within 5 months of initiation. These molecules demonstrated significant potential in silico, paving the way for further efficacy confirmation studies by QIMRB.
Breakthrough in Treating Lung Diseases: The AI-identified drug candidates have shown promising results in reducing inflammatory markers such as CCL2 and IRF1 in PBMCs (Peripheral Blood Mononuclear Cells) from COPD patients. Additionally, these compounds were effective in reducing lung damage, representing a potential breakthrough in the treatment of lung cancer, a disease with a 5-year survival rate of less than 18%.
GlobaI lmpact on Lung Cancer and COPD Treatment: Lung cancer remains the second most common cancer worldwide and a leading cause of death, with 1 in 16 people being diagnosed in their lifetime. The success of this collaboration between Syntekabio and QIMRB offers hope for novel immunotherapy candidates that can address the imitations of current treatments, such as resistance to therapy and cancer recurrence.
DeepMatcher® Success Stories
DeepMatcher® hit generation - Our Auto-BP Tool Outperforms Industry Benchmarks in molecular docking accuracy
Syntekabio’s Auto-BP (Auto-Binding Pose) tool, part of DeepMatcher®, and its advanced accuracy in molecular docking enable drug developers to streamline the early stages of drug discovery. By identifying the best-fitting compounds for the binding site of a target protein of interest faster and more reliably, researchers can significantly accelerate the pipeline from hit identification to lead optimization, ultimately bringing new therapies to market more swiftly.
Enhanced Precision: Auto-BP consistently achieves an RMSD (Root Mean Square Deviation from original structure) of less than 2.5, which is considered the gold standard for predicting binding accuracy. This metric indicates that Auto-BP can reliably identify how closely a drug fits into a protein’s active site, crucial for effective drug design. I don’t know if you need to spell this out or not...
Benchmark Success: When compared to leading tools in the industry (DINC & IFD-MD), Auto-BP showed higher success rates in molecular docking, making it an indispensable tool for researchers aiming to shorten the drug development timeline.
Real-World Applications: The superior performance of Auto-BP not only reduces the time spent on early drug discovery but also minimizes the costs associated with failed experiments. This allows pharmaceutical companies to focus resources on the most promising drug candidates.
DeepMatcher® Success Stories
The Superiority of Syntekabio's Flexible Molecular Docking (FMD) Approach in Drug Discovery
Syntekabio’s AI-driven DeepMatcher® platform leverages Flexible Molecular Docking (FMD) to offer substantial improvements over traditional Rigid Molecular Docking (RMD). By incorporating protein flexibility, FMD enhances the accuracy of pose predictions, increases the likelihood of discovering viable drug candidates, and can potentially shorten development timelines, providing a significant edge in drug discovery.
Enhanced Accuracy in Pose Predictions: FMD accounts for the natural flexibility of proteins, leading to more accurate pose predictions, which can achieve accuracy levels between 80-95%. This is a notable improvement over RMD, which typically offers accuracy rates of 50-75%. This higher precision aids in more confidently identifying potential drug candidates.
Increased Likelihood of Viable Hits: FMD’s flexible approach expands the opportunity to identify promising hits and leads, often resulting in 6-8 good hits and 3-4 optimized leads. In comparison, RMD tends to produce fewer hits, often only 1-2, due to its rigid nature. This broader range of potential candidates early in the process enhances the chances of finding effective therapies.
Potential for Faster Development: FMD has the capability to reduce the overall timeline for drug discovery, potentially shortening the process from the typical 5-7 years associated with RMD to as little as 2 years. This accelerated timeline can bring viable drug candidates to the preclinical stage more quickly, enhancing the efficiency of the drug development pipeline.
DeepMatcher® Success Stories
DeepMatcher® lead generation and optimization too, ALGO Delivers Significant Improvements
Syntekabio’s ALGO (Auto-Lead Generation & Optimization) tool rapidly and significantly enhances the efficacy of lead compounds, giving clients a strategic advantage. By shortening the lead optimization phase, ALGO allows companies to move promising candidates into preclinical and clinical trials more quickly, reducing the overall time and cost of drug development. This is particularly critical in areas like oncology, where speed can directly impact patient outcomes.
CLK2 Inhibitor: Within just 5 months, ALGO optimized a CLK2 inhibitor, enhancing its efficacy by 32-fold. This dramatic improvement underscores ALGO’s potential to refine drug candidates quickly and effectively.
IDO/TDO Dual Inhibitor (STB-C017): ALGO was also instrumental in optimizing a dual inhibitor targeting IDO and TDO, two critical enzymes in immune-oncology. Over 12 months, ALGO improved the inhibitor’s potency by 14-fold, showcasing the platform’s capability in complex therapeutic areas.
FLT3 Inhibitor: For the FLT3 target, ALGO achieved a 6.5-fold improvement in efficacy in just 5 months, with 6 out of 7 optimized compounds showing significant enhancements. This quick turnaround is vital for staying ahead in competitive therapeutic landscapes.
DeepMatcher® Success Stories
Biologics: In Silico Antibody - PD-L1 Case Study
Syntekabio’s Ab-ARS™ platform demonstrates the power of in silico methods in biologics discovery. By delivering antibody candidates that outperform existing drugs, the platform offers pharmaceutical companies a faster and more cost-effective route to developing next-generation therapies. This is particularly important in the context of cancer treatment, where the ability to quickly develop effective, targeted therapies can have a profound impact on patient survival rates.
Superior Antibody Candidate: Ab-ARS™ identified a new antibody candidate that binds more effectively to the PD-L1 protein than atezolizumab, an approved PD-L1 inhibitor used in cancer therapy. This improved binding affinity suggests that the new candidate could potentially offer better therapeutic outcomes.
Efficient Discovery Process: The in silico (computer-based) approach used by Ab-ARS™ significantly speeds up the discovery process. By leveraging advanced algorithms, the platform can screen vast libraries of compounds and predict their interactions with target proteins much faster than traditional methods.
DeepMatcher® Success Stories
Metaclipse Collaboration: Personalized NeoAntigen Prediction
The NEO-ARS™ platform’s success in identifying and validating neoantigens represents a significant reakthrough in personalized medicine. By enabling the development of therapies tailored to individual patients’ unique cancer profiles, Syntekabio is helping to pave the way for more effective and less toxic cancer treatments. This approach not only improves patient outcomes but also opens new avenues for research in immuno-oncology.
NEO-ARS™ Platform: Syntekabio’s platform accurately identifies neoantigens—unique markers on cancer cells that can be targeted by the immune system. These neoantigens are validated using ex vivo IFN-γ ELISpot assays, confirming the platform’s precision in predicting effective targets for personalized cancer therapies.
High-Purity Synthesis: The neoantigens identified by NEO-ARS™ were synthesized with over 95% purity, ensuring that the resulting peptides were highly specific to the patient’s cancer cells. This high level of specificity is crucial for effective personalized therapies.
Immune Response: The synthesized peptides successfully activated T-cells, a critical component of the immune response against cancer. This demonstrates the platform’s potential to generate effective, patient-specific cancer treatments that harness the body’sown immune system.
FDA Clearance: Metaclipse’s proprietary cancer vaccine, supported by Syntekabio’s technology, has received clearance from the US FDA to proceed with a phase 1 clinical trial. This marks a significant milestone in the development of the vaccine, which leverages patient-specific neoantigens identified by NEO-ARS™.