Dr. Kirsten Bischof & Gaurav Chandra
“Innovation is the unrelenting drive to break the status quo and develop anew where few have dared to go.” – Steven Jeffes.
As a revolutionary tool in any domain, one of AI’s most significant selling points is its ability to process data quantities that the human mind would struggle to comprehend. With the incredible advancements in technology, we can now accumulate and preserve an immense amount of digital patient data. This valuable collection of health records, genomic information, imaging, and other patient health details can be utilized by AI platforms to expedite drug development. AI can revolutionize drug discovery by unlocking new biological insights, identifying innovative or improved chemistry, increasing success rates in clinical trials, and streamlining the discovery process to be more efficient and cost-effective.
The market for AI in drug discovery is expected to grow substantially, with a projected revenue of approximately $4 billion by 2027, up from $0.6 billion in 2022. [1]
Pharmaceutical companies face high costs and lengthy timelines when discovering new drugs. Artificial intelligence serves as a tool that reduces the wait.
The use of AI has the potential to significantly impact drug development by identifying better drug candidates, increasing success rates, and accelerating the overall process. Major pharmaceutical companies invest in internal algorithms and partner with startups to leverage AI and expand their portfolios. The industry is shifting towards creating AI platforms to identify solutions for multiple drug targets across various domains, from infectious diseases to cancer treatments, rather than single-use models. The scientific community is eagerly anticipating the future advancements in this field.
To achieve success in this area, several key factors must be considered.
- Firstly, newly discovered drugs must surpass the effectiveness of existing options.
- Secondly, regulatory agencies have heightened standards for drug safety and clinical trials.
- Thirdly, the pharmaceutical industry is experiencing greater profitability due to the increased availability of capital, driving investment in research and development.
- Finally, a shift towards identifying drug candidates through basic research and improvements in early-stage R&D costs may lead to more candidates advancing to clinical trials but with a lower likelihood of success.
Using AI technology in drug discovery can significantly improve four key areas.
- It can grant access to previously unexplored avenues of biology, leading to breakthroughs in developing new treatments.
- It can identify improved chemistry that can result in better drugs.
- It can provide more accurate drug efficacy and safety predictions and help ensure better clinical trial success rates.
- Finally, it can speed up the discovery process and reduce the time and resources required to bring new drugs to market.
At Biogenysis, we have recognized that integrating biology, technology, and innovation is imperative in the fight against diseases. As a dedicated drug development company, we aim to diagnose, prevent, and treat various illnesses using our ever-evolving AI Platform, Chandradrishti AI. This wholly-owned trademarked Artificial Intelligence Platform facilitates early drug discovery and development while aiding in creating an intellectual property portfolio. Furthermore, our AI-empowered Monoclonal Antibody Platform is dedicated to developing species-specific monoclonal antibodies for human and animal use.
Biogenysis Chandradrishti Artificial Intelligence Platform
Our company, Biogenysis, has developed a unique and innovative approach to producing anti-bodies targeting specific virus sites. This process involves using Artificial Intelligence to identify conserved and immutable targets, which we can then analyze for linearity, accessibility by antibodies, neutralizability, and the effect of mutations. This is significant, especially since no other organism in the kingdom of life can match the high mutation rates of viruses. The high mutation rates of viruses, along with short generation times and large population sizes, allow viruses to evolve and adapt to the host environment rapidly.
Solving the HIV Virus dilemma
HIV viruses exhibit a significant amount of genetic diversity. [1]
This virus has a swift rate of copying its genetic blueprint, producing thousands of new copies daily in a single person. As a result, a person may possess various virus variants within their body. The primary obstacle to treating HIV is its ability to insert its genetic blueprint into host DNA, which creates a hidden reservoir in immune cells called T cells. This makes the virus invisible to the immune system, making it difficult to eradicate with both drug treatments and the immune system.
Biogenysis is developing Anti-HIV Monoclonal Antibodies that target conserved targets that have remained unchanged for 30 years[2]
These Monoclonal Antibodies are more potent than those currently being developed. Our advanced AI platform has showcased the pivotal role of artificial intelligence in drug discovery. Through its rigorous analysis and calculations, it has not only confirmed the initial target but also unearthed seven additional targets in just two weeks. Additionally, three 3-dimensional models of these conserved targets are generated, and the targets are analyzed for linearity, accessibility by antibodies, and neutralizability by antibodies. The antibodies are selected based on an AI algorithm and rigorous antibody characterization. This highlights AI’s immense potential in revolutionizing how we approach drug development and ultimately improving patient outcomes. The AI platform Chandradrishti is aligned with pharmaceutical companies’ interest in AI pipeline-building platforms. Big pharma companies are developing internal algorithms and partnering with or acquiring new companies to use AI to increase their portfolios and pipelines. The industry is moving towards developing AI platforms that enable the identification of solutions for a host of drug targets in any domain – from infectious diseases to cancer therapeutics – as opposed to single-use models. More activity will be in this arena in the coming years, and we are incredibly excited about what will evolve.
P4 Medicine [3][4]
The healthcare industry has evolved from a reactive to a proactive approach. The “one-size-fits-all” approach is no longer practical. Personalized medicine replaces traditional approaches with tailored diagnostic, treatment, and preventative measures designed specifically for each patient’s requirements. This approach will carefully consider each patient’s unique characteristics, including their genetics, lifestyle, and medical history.
The Human microbiome will play a crucial role in this transformation, enabling precision medicine that is more preventive, predictive, and personal than ever before. [5]
Developing advanced methods for analyzing microbiome data to derive actionable insights becomes increasingly essential. The microbiome field moves from associations to causality, mechanisms, and prediction, and machine learning (ML) will aid in this transition. Data obtained from ML methods can help propose new hypotheses to be tested in experimental models and accelerate the translation of the microbiome data into clinical practice. Its optimal use will trigger the improvement of sourcing biomarker candidates for disease diagnostics, prognostics, and statistical inference for causal insights.
Biogenysis Human Microbiome Consortium
Our consortium recognizes the Human Microbiome’s significance and AI’s role in discovering new pathways, biomarkers, and treatment options for various diseases. We seek to expand our membership to explore further the impact of nutrition, genetics, and microbiome on conditions. Our consortium includes the Ministry of Health, Estonia, the Estonian entities Contenta, the Estonian Genome Foundation, and Khalpey AI lab. We welcome participation from universities and biotechnology companies to achieve our threefold goals:
- Develop predictive models for diseases
- Identify biomarkers for diseases
- Determine novel targets and effective intervention strategies for diseases
The outcomes of our research will significantly improve the early detection and treatment of various diseases. Our consortium provides opportunities for analyzing a wide range of diseases.
Conclusion
Biogenysis strives to make a difference in health and wellness through strong partnerships with research, technology, and development experts. Our carefully selected partners bring their knowledge and leadership, allowing us to provide accessible and affordable therapeutic solutions. By combining the power of AI with collaborative efforts, we can redefine the African proverb, “If you want to go fast, go alone; if you want to go far, go together,” and achieve efficiency, longevity, and sustainability. We understand the importance of progress and evolution in achieving our goals and remain committed to pushing forward toward our aspirations. Onwards and upwards, we march on.
