Regenova Pharma

Regenova Pharma is revolutionizing drug discovery with its proprietary AI-driven platform, focused on developing novel antibodies and nanobodies to target infectious diseases. With our deep learning models, we drastically cut down discovery timelines and costs, offering pharma partners cutting-edge therapeutic solutions. Our experienced team combines industry and academic expertise, positioning Regenova as a leader in AI-based drug development.

Regenova Pharmaceuticals has developed a novel deep learning model for therapeutic antibody discovery targeting infectious diseases. Their validated system achieves a Mean Absolute Error (MAE) of 1.3, underscoring its precision and effectiveness. This model was thoroughly cross-validated and is disease-specific, designed exclusively for discovering antibodies against viral antigens. This model can also generate a 3D structure of the predicted antibody.

Regenova is now looking to collaborate with academia labs at Drexel University for further testing of the predicted antibody(s).

NSF SBIR grants application in process.

### Customer Description:

- **Customer Type**: Biotechnology and pharmaceutical companies, academic research institutions, and government agencies involved in infectious disease and oncology research.
- **Location**: Primarily North America, Europe, and Asia, with a growing focus on emerging biotech hubs in India and China.
- **Target Age Group**: Professionals and decision-makers aged 35-60, including scientists, researchers, and executives in drug discovery, R&D, and innovation departments.
- **Other Characteristics**: These customers are typically looking for advanced, AI-driven tools to expedite drug discovery, reduce costs, and improve efficiency. They value cutting-edge technology, innovation, and collaboration in therapeutic development, particularly in addressing unmet needs in infectious diseases and oncology.

### Market Size and Development:

- **Global Market**: The global AI-driven drug discovery market was valued at approximately $800 million in 2020 and is projected to grow at a compound annual growth rate (CAGR) of 40%+, reaching around $8 billion by 2030. The biopharmaceutical market itself, which heavily overlaps with Regenova's target customers, is valued at over $325 billion globally.

- **Local Market**: In North America, the AI drug discovery market leads with nearly 50% market share, valued at approximately $400 million in 2020. Europe follows with a growing focus on AI integration in pharma, while Asia-Pacific markets, particularly in India and China, are rapidly expanding.

### Market Trends:

- **Rising**: The AI-driven drug discovery market is on an upward trajectory, driven by the increasing demand for faster, cost-effective drug development processes and the rising adoption of AI/ML technologies in pharmaceutical research. Government funding, as well as partnerships between academic institutions and biotech firms, further accelerate growth.

Regenova is solving the inefficiencies in traditional drug discovery by using AI and deep learning models to accelerate the development of therapeutic antibodies, reducing time, cost, and increasing success rates.

Regenova’s solution leverages a proprietary deep learning model that specifically predicts therapeutic antibodies for viral. Our AI-driven platform generates accurate antibody sequences, including heavy and light chains, and provides 3D structures, significantly reducing the time and cost of drug discovery while improving success rates in developing effective treatments for infectious diseases.

1. AbCellera Biologics
• Overview: A Canadian biotech firm that leverages AI to streamline antibody discovery and development.
• Focus Areas:
o Therapeutic Areas: Oncology, infectious diseases, and immunology.
o Technology: AI-powered platform to rapidly identify therapeutic antibodies, including those for viral infections.
• Strengths:
o Proven ability to discover therapeutic antibodies quickly, with notable successes like bamlanivimab (for COVID-19).
o Strong partnerships with leading pharmaceutical companies.
• Weaknesses:
o Focuses more on general antibody discovery, with limited specialization in ADCs or nanobodies.
o High reliance on partnerships for downstream development.

2. Zymeworks
• Overview: Specializes in multi-specific antibodies and ADCs for oncology treatments.
• Focus Areas:
o Therapeutic Areas: Oncology and inflammatory diseases.
o Technology: Focuses on bi-specific antibodies and ADCs, with an established pipeline for cancer treatment.
• Strengths:
o Significant expertise in bi-specific antibodies and ADC development.
o Strong position in oncology through partnerships with large pharmaceutical companies.
• Weaknesses:
o Limited scope outside oncology; little focus on infectious diseases or non-ADC therapeutic approaches.
o ADCs carry clinical development risks due to toxicity concerns.

3. Genmab
• Overview: A biotech company specializing in antibody therapeutics, focusing on oncology and autoimmune diseases.
• Focus Areas:
o Therapeutic Areas: Primarily oncology and autoimmune diseases, with limited focus on infectious diseases.
o Technology: Bi-specific antibodies (DuoBody® platform) and ADCs for targeted therapies.
• Strengths:
o Proven success in oncology with several products in clinical trials or on the market.
o Strong partnerships with major pharmaceutical companies.
• Weaknesses:
o ADC development poses challenges related to safety and efficacy.
o Limited focus on infectious diseases or pandemic preparedness.

4. ImmunoGen
• Overview: Focused on the development of ADCs for oncology, with a robust pipeline of targeted cancer therapies.
• Focus Areas:
o Therapeutic Areas: Primarily focused on solid tumors in oncology.
o Technology: Proprietary ADC platform used for drug discovery and development.
• Strengths:
o Extensive experience with ADC development, positioning it as a leader in the space.
o Established partnerships with pharmaceutical companies to co-develop ADC therapies.
• Weaknesses:
o Focuses exclusively on oncology, leaving gaps in infectious disease areas.
o ADC development risks, particularly related to toxicity, could lead to regulatory hurdles.

5. MacroGenics
• Overview: Specializes in antibody-based therapeutics, including bi-specific antibodies and ADCs.
• Focus Areas:
o Therapeutic Areas: Oncology and autoimmune diseases, with a pipeline of bi-specific antibodies and ADCs.
• Strengths:
o Known for bi-specific antibody development and clinical pipeline in ADCs.
o Strong clinical focus, with several candidates in trials.
• Weaknesses:
o ADC technology presents risks, especially concerning delivery and safety.
o Limited involvement in infectious diseases or pandemic preparedness.

1. Oncology: ADC and NDC Development:
• Opportunities:
o Develop a first-in-class therapy for pancreatic cancer using biparatopic NDCs.
o Focus on cancers that are hard to treat with traditional therapies (e.g., triple-negative breast cancer).
• Challenges:
o Regulatory hurdles and clinical challenges related to ADC/NDC safety and efficacy.
2. Infectious Diseases: Pandemic Preparedness and Pediatric Focus:
• Opportunities:
o Lead in pandemic preparedness by targeting Nipah virus and pediatric herpes simplex infections.
o Few competitors focus on this space, offering Regenova a chance to establish dominance in pediatric infectious diseases.
• Challenges:
o Ensuring that antibody-based therapies for infectious diseases achieve strong efficacy and attract funding in a space traditionally dominated by vaccines and antivirals.

Threats and Challenges
1. ADC/NDC Development Risks:
o Toxicity and Safety: ADCs and NDCs, while promising, carry risks related to toxicity and drug delivery, which could delay clinical development or regulatory approval.
o Competition from Established Players: Companies like ImmunoGen and Zymeworks already have established ADC pipelines, which may pose competitive pressure in oncology.
2. Funding Constraints:
o Investment Requirements: Developing antibodies for both oncology (with ADC/NDC applications) and viral diseases requires significant capital. Delays in securing investment could slow down key R&D milestones.
3. Technological Evolution:
o Keeping Ahead in AI: As AI-driven antibody discovery continues to evolve rapidly, Regenova will need to ensure that its TRACE™ platform remains at the cutting edge of antibody creation technologies.

Revenue Streams:
1. **SaaS Model**: Licensing AI-driven drug discovery software to biotech and pharmaceutical companies on a subscription basis.
2. **Pharma Collaborations**: Co-development agreements with pharmaceutical firms, generating revenue from milestone payments, royalties, and licensing fees.
3. **Academic & Government Partnerships**: Collaborations with universities and research institutions, generating revenue through grants, research contracts, and IP licensing.
4. **In-house Drug Development**: Revenue from proprietary drug candidates through licensing, commercialization, or sale.

Cost Structure:
1. **Research & Development**: Major investment in AI model development, data acquisition, preclinical studies, and lab testing.
2. **Personnel**: Salaries for data scientists, AI experts, lab technicians, and key management.
3. **Equipment & Lab Space**: Costs for lab setup, equipment purchases, and maintenance.
4. **Regulatory & Legal**: Expenses related to FDA approvals, IP protection, and patent filings.
5. **Marketing & Business Development**: Costs for marketing, partnerships, and customer acquisition strategies.

Regenova Pharma leverages cutting edge deep learning models with >90% accuracy, surpassing industry standards. They specialize in large molecule drug discovery, focus on infectious diseases and oncology, and
looking for strategic partnerships with leading Pharma and Biotech companies.

Regenova is seeking to raise $500K USD to advance the predicted antibody feasibility into the wet lab. Funds will be allocated toward critical experiments such as binding affinity testing, cell neutralization, plaque assays, and animal model testing. Additionally, we plan to hire data scientists and invest in lab space, equipment, and resources to further accelerate our AI-driven drug discovery platform.

$500K for 5% equity (SAFE)

Regenova faces several potential risks that could impact its success. Competitors, especially larger biotech firms, may develop more advanced or faster AI-driven drug discovery platforms, increasing market competition. A global crisis, such as economic downturns or disruptions in supply chains, could slow down progress or limit access to funding. Additionally, rapid advancements in new technologies could potentially outpace our model, requiring continual adaptation to maintain a competitive edge. Regulatory hurdles or delays in clinical validation could also pose significant challenges.

H7 Biocapital program

N/A