UNITED STATES SECURITIES AND EXCHANGE COMMISSION

Washington, D.C. 20549

FORM 10-K

(Mark One)

☒ ANNUAL REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934

For the fiscal year ended: December 31, 2024

☐ TRANSITION REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934

For the transition period from [ ] to [ ]

Commission file number 001-41600

BULLFROG AI HOLDINGS, INC.

(Exact name of registrant as specified in its charter)

Nevada

84-4786155

(State or other jurisdiction of

incorporation or organization)

(I.R.S. Employer

Identification No.)

325 Ellington Blvd., Unit 317

Gaithersburg, MD 20878

(Address of principal executive offices)

Registrant’s telephone number: (240) 658-6710

Securities registered pursuant to Section 12(b) of the Act:

Title of each class

Trading

Symbol(s)

Name of each exchange on which registered

Common Stock, par value $0.00001 per share

BFRG

The Nasdaq Stock Market LLC (The Nasdaq Capital Market)

Tradable Warrants

BFRGW

The Nasdaq Stock Market LLC (The Nasdaq Capital Market)

Securities registered pursuant to Section 12(g) of the Act:

None

Indicate by check mark if registrant is a well-known seasoned issuer, as defined under Rule 405 of the Securities Act. Yes ☐ No ☒

Indicate by check mark if registrant is not required to file reports pursuant to Section 13 or Section 15(d) of the Act. Yes ☐ No ☒

Indicate by check mark whether the registrant (1) has filed all reports required to be filed by Section 13 or 15(d) of the Securities Exchange Act during the preceding 12 months (or for such shorter period that the issuer was required to file such reports), and (2) has been subject to such filing requirements for the past 90 days. Yes ☒ No ☐

Indicate by check mark whether the registrant has submitted electronically every Interactive Data File required to be submitted pursuant to Rule 405 of Regulation S-T (§232.405 of this chapter) during the preceding 12 months (or for such shorter period that the registrant was required to submit such files). Yes ☒ No ☐

Indicate by check mark whether the registrant is a large accelerated filer, an accelerated filer, a non-accelerated filer, smaller reporting company, or an emerging growth company. See the definitions of “large accelerated filer,” “accelerated filer,” “smaller reporting company,” and “emerging growth company” in Rule 12b-2 of the Exchange Act.

Large accelerated filer	☐	Accelerated filer	☐
Non-accelerated filer	☒	Smaller reporting company	☒
		Emerging growth company	☒

If an emerging growth company, indicate by check mark if the registrant has elected not to use the extended transition period for complying with any new or revised financial accounting standards provided pursuant to Section 13(a) of the Exchange Act. ☐

Indicate by check mark whether the registrant has filed a report on and attestation to its management’s assessment of the effectiveness of its internal control over financial reporting under section 404(b) of the Sarbanes-Oxley Act (15 U.S.C. 7262(b)) by the registered public accounting firm that prepared or issued its audit report. ☐

If securities are registered pursuant to Section 12(b) of the Act, indicate by check mark whether the financial statements of the registrant included in the filing reflect the correction of an error to previously issued financial statements. ☐

Indicate by check mark whether any of those error corrections are restatements that required a recovery analysis of incentive-based compensation received by any of the registrant’s executive officers during the relevant recovery period pursuant to §240.10D-1(b). ☐

Indicate by check mark whether the registrant is a shell company (as defined in Rule 12b-2 of the Act). Yes ☐ No ☒

As of June 30, 2024, the aggregate market value of the common stock of the registrant held by non-affiliates was approximately $9.0 million. Shares of common stock held by each officer and director of the registrant on June 30, 2024 have been excluded in that such persons may be deemed to be affiliates.

The number of shares of the registrant’s common stock outstanding as of March 11, 2025 was 9,415,525.

Documents incorporated by reference

None.

TABLE OF CONTENTS

		Page
Part I
Item 1	Business	4
Item 1A	Risk Factors	22
Item 1B	Unresolved Staff Comments	22
Item 1C	Cybersecurity	22
Item 2	Properties	23
Item 3	Legal Proceedings	23
Item 4	Mine Safety Disclosures	23
Part II
Item 5	Market for Registrant’s Common Equity, Related Stockholder Matters and Issuer Purchases of Equity Securities	24
Item 6	Reserved	24
Item 7	Management’s Discussion and Analysis of Financial Condition and Plan of Operation	24
Item 7A	Quantitative and Qualitative Disclosures About Market Risk	30
Item 8	Financial Statements and Supplementary Data	30
Item 9	Changes in and Disagreements with Accountants on Accounting and Financial Disclosure	30
Item 9A	Controls and Procedures	30
Item 9B	Other Information	31
Item 9C	Disclosure Regarding Foreign Jurisdictions that Prevent Inspections	31
Part III
Item 10	Directors, Executive Officers and Corporate Governance	32
Item 11	Executive Compensation	37
Item 12	Security Ownership of Certain Beneficial Owners and Management and Related Stockholder Matters	39
Item 13	Certain Relationships and Related Transactions, and Director Independence	41
Item 14	Principal Accounting Fees and Services	42
Part IV
Item 15	Exhibits and Financial Statement Schedules	43
Item 16	Form 10-K Summary	44
Signatures		45

In this report, unless the context indicates otherwise, the terms “Company,” “we,” “us,” “our” and similar words refer to Bullfrog AI Holdings, Inc. (“Bullfrog”), a Nevada corporation.

SPECIAL NOTE REGARDING FORWARD-LOOKING STATEMENTS

This report contains forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, or the “Securities Act,” and Section 21E of the Securities Exchange Act of 1934 or the “Exchange Act.” These forward-looking statements are subject to certain risks and uncertainties that could cause actual results to differ materially from historical results or anticipated results.

In some cases, you can identify forward-looking statements by terms such as “may,” “intend,” “might,” “will,” “should,” “could,” “would,” “expect,” “believe,” “anticipate,” “estimate,” “predict,” “potential,” or the negative of these terms. These terms and similar expressions are intended to identify forward-looking statements. The forward-looking statements in this report are based upon management’s current expectations and beliefs, which management believes are reasonable. In addition, we cannot assess the impact of each factor on our business or the extent to which any factor or combination of factors, or factors we are aware of, may cause actual results to differ materially from those contained in any forward-looking statements. You are cautioned not to place undue reliance on any forward-looking statements. These statements represent our estimates and assumptions only as of the date of this report. Except to the extent required by federal securities laws, we undertake no obligation to update any forward-looking statement to reflect events or circumstances after the date hereof or to reflect the occurrence of unanticipated events.

You should be aware that our actual results could differ materially from those contained in the forward-looking statements due to a number of factors, including:

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our future financial performance, including our revenue, costs of revenue, operating expenses and profitability;

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the sufficiency of our cash and cash equivalents to meet our liquidity needs;

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our predictions about, and the development of, digital transformation technology and bio health businesses and their respective market trends;

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our ability to attract and retain customers for our products and services in each of our business segments;

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the availability of financing for smaller publicly traded companies like us;

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our current and future capital requirements to support the continued development and commercialization of our products and services;

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our ability to successfully expand in our three principal business markets and into new markets and industry verticals; and

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our ability to effectively manage our growth and future expenses.

Other risks and uncertainties include such factors, among others, as market acceptance and market demand for our products and services, pricing, the changing regulatory environment, the effect of our accounting policies, industry trends, adequacy of our financial resources to execute our business plan, our ability to attract, retain and motivate key personnel, and other risks described from time to time in periodic and current reports we file with the United States Securities and Exchange Commission, or the “SEC.” You should consider carefully the statements under this report, which address additional factors that could cause our actual results to differ from those set forth in the forward-looking statements and could materially and adversely affect our business, operating results and financial condition. All subsequent written and oral forward-looking statements attributable to us or persons acting on our behalf are expressly qualified in their entirety by the applicable cautionary statements.

PART I

ITEM 1. BUSINESS

Our Corporate History and Background

Bullfrog AI Holdings, Inc. was incorporated in the State of Nevada on February 6, 2020. Bullfrog AI Holdings, Inc. is the parent company of Bullfrog AI, Inc. and Bullfrog AI Management, LLC, which were incorporated in Delaware and Maryland, in 2017 and 2021, respectively. All our operations are currently conducted through Bullfrog AI Holdings, Inc. The Company’s principal business address is 325 Ellington Blvd, Unit 317, Gaithersburg, MD 20878. Our website address is www.bullfrogai.com. The references to our website in this Annual Report on Form 10-K (the “Form 10-K”) are inactive textual references only. The information on our website is not incorporated into this Form 10-K.

Acquisition of Bullfrog AI, Inc.

In June 2020, Bullfrog AI Holdings, Inc. acquired Bullfrog AI, Inc. pursuant to an exchange agreement under which each share of Bullfrog AI, Inc. common stock was exchanged for a share of common stock of the Company. Immediately prior to the share exchange, each outstanding common share of Bullfrog AI, Inc. was split into 25 shares of common stock. Pursuant to the share exchange agreement, 24,223,975 shares of the Company’s common stock were issued to the shareholders of Bullfrog AI, Inc. in exchange for 100% of the outstanding stock of Bullfrog AI, Inc. Upon completion of the exchange, Bullfrog AI, Inc. became the Company’s wholly-owned subsidiary and the shareholders of Bullfrog AI, Inc. held 100% of the common stock of the Company. As a result, the Company assumed a total of $330,442 in net liabilities of Bullfrog AI, Inc. Both of the entities were controlled before and after the transactions by the same controlling shareholder.

Bullfrog AI Corporate History

Bullfrog AI, Inc. was incorporated in the State of Delaware on August 25, 2017. Vininder Singh, the Chief Executive Officer, was the founder, CEO and chairman of Bullfrog AI, Inc.

Business Overview

Most new therapeutics will fail at some point in preclinical or clinical development. This is the primary driver of the high cost of developing new therapeutics. A major part of the difficulty in developing new therapeutics is efficient integration of complex and highly dimensional data generated at each stage of development to de-risk subsequent stages of the development process. Artificial Intelligence and Machine Learning (“AI/ML”) has emerged as a digital solution to help address this problem.

We use artificial intelligence and machine learning to advance medicines for both internal and external projects. We are committed to increasing the probability of success and decreasing the time and cost involved in developing therapeutics. Most current AI/ML platforms still fall short in their ability to synthesize disparate, high-dimensional data for actionable insight. Our platform technology, named, bfLEAP™, is an analytical AI/ML platform derived from technology developed at The Johns Hopkins University Applied Physics Laboratory (“JHU-APL”), which is able to surmount the challenges of scalability and flexibility currently hindering researchers and clinicians by providing a more precise¹, multi-dimensional understanding of their data. We are deploying bfLEAP™ for use at several critical stages of development for internal programs and through strategic partnerships and collaborations with the intention of streamlining data analytics in therapeutics development, decreasing the overall development costs by decreasing failure rates for new therapeutics, and impacting the lives of countless patients that may otherwise not receive the therapies they need.

1 In an August 2021 publication in DeepAI.org (https://deepai.org/publication/random-subspace-mixture-models-for-interpretable-anomaly-detection), the algorithms used in bfLEAP were compared to 10 of the most popular clustering algorithms in the world using 12 data sets. The end result showed that the algorithms used in bfLEAP had the highest average score when measuring speed and accuracy of prediction. The bfLEAP platform currently has more advanced versions of these algorithms and is applying them in multiple data analytics projects.

Recent Developments

In February 2025, we announced our entry into a collaboration agreement with Eleison Pharmaceuticals Inc. (“Eleison”), a Phase III oncology company focused on novel chemotherapeutic treatments for rare cancers. Through this collaboration, we will apply our proprietary Bullfrog Data Networks™ solution, powered by the bfLEAP® platform, to analyze clinical data from Eleison’s ongoing Phase III trial and previous clinical studies of glufosfamide, an investigational treatment for pancreatic cancer. The platform will evaluate the current trajectory of the trial with respect to safety signals, extract predictive biomarkers for efficacy and safety performance from prior studies to support future trial design, and provide data-driven insights to optimize Eleison’s planned clinical trials for inhaled lipid-complexed cisplatin (ILC) and dibromodulcitol (DBD). These insights are expected to streamline trial efficiency and improve decision-making for Eleison’s broader oncology pipeline.

Our Strategy

We plan to achieve our business objectives by enabling the successful development of drugs and biologics using a precision medicine approach via our proprietary artificial intelligence platform bfLEAP. The bfLEAP™ platform utilizes both supervised and unsupervised machine learning; as such, it is able to reveal meaningful connections in the data without the need for a prior hypothesis. Supervised machine learning uses labeled input and output data, while an unsupervised learning algorithm does not. In supervised learning, the algorithm “learns” from the training dataset by iteratively making predictions on the data and adjusting for the correct answer. Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention. Algorithms used in the bfLEAP™ platform are designed to handle highly imbalanced data sets to successfully identify combinations of factors that are associated with outcomes of interest.

Together with our strategic partners and collaborators, our primary goal is to improve the odds of success at any stage of pre-clinical and clinical therapeutics development. Our primary business model is improving the success and efficiency of drug development which is accomplished either through acquisition of drugs or partnerships and collaborations with companies that are developing drugs. We hope to accomplish this through strategic acquisitions of current clinical stage and failed drugs for in-house development, or through strategic partnerships with biopharmaceutical industry companies. We are able to pursue our drug asset enhancement business by leveraging a powerful and proven AI/ML platform (trade name: bfLEAP™) initially derived from technology developed at JHU-APL. We believe the bfLEAP™ analytics platform is a potentially disruptive tool for analysis of pre-clinical and clinical data sets, such as the robust pre-clinical and clinical trial data sets being generated in translational R&D and clinical trial settings. In November 2021, we amended our license agreement with JHU-APL to include additional advanced AI technology. On July 8, 2022, the Company entered into an exclusive, world-wide, royalty-bearing license from JHU-APL for the additional technology developed to enhance the bfLEAP™ platform. The July 8, 2022 JHU-APL license provides the Company with new intellectual property and also encompasses most of the intellectual property from our original February 2018 license agreement with JHU-APL.

We believe bfLEAP™ will inform and enable decision making throughout the development cycle:

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Discovery Phase - Analyze and categorize discovery phase data to better define highest-value leads from groups of candidates, for advancement to preclinical phase of development. Integrate data from high-throughput screening, pharmacodynamics assays, pharmacokinetics assays, and other key data sets to create the most accurate profile of a pool of therapeutic candidates. There is often a high degree of similarity among closely related therapeutics in a candidate pool - bfLEAP™ is able to harmonize disparate data streams for a more nuanced understanding of each candidate’s characteristics/potency.

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Pre-Clinical Data - Large-scale, multivariate analysis of pre-clinical and early-stage clinical data sets. In these settings, bfLEAP could be used to find novel drug targets, elucidate mechanism of action, predict potential off-target effects/side effects, uncover specific genetic/phenotypic background(s) with highest correlation to therapeutic response, etc. These insights from bfLEAP™ analysis can be used to inform decision making and study design at the subsequent step(s) of therapeutic and diagnostic development, including first-inhuman/Phase I randomized controlled trials.

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Clinical Development – Advanced, multivariate analysis of Phase I and Phase II clinical trials data, to find niche populations of highly responsive patients and/or inform patient selection for later-stage clinical trials. This can be used to decrease overall study risk for larger clinical trials - including Phase II trials, and any Phase III Registration Clinical Trials. The bfLEAP™ platform analysis can also be used to more precisely understand complex correlations between therapeutic treatment and adverse events, side effects, and other undesirable responses which could jeopardize clinical trial success.

Our platform is agnostic to the disease indication or treatment modality and therefore we believe that it is of value in the development of biologics or small molecules.

The process for our drug asset enhancement program is to:

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acquire the rights to a drug from a biopharmaceutical industry company or academia;

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use the proprietary bfLEAP™ AI/ML platform to determine a multi-factorial profile for a patient that would best respond to the drug;

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rapidly conduct a clinical trial to validate the drug’s use for the defined “high-responder” population; and

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divest and sell the rescued drug asset with the new information back to a large player in the pharma industry, following positive results of the clinical trial.

As part of our strategy, we will continue evolving our intellectual property, analytical platform and technologies, build a large portfolio of drug candidates, and implement a model that reduces risk and increases the frequency of cash flow from rescued drugs. This strategy will include strategic partnerships, collaborations, and relationships along the entire drug development value chain, as well as acquisitions of the rights to developing failed drugs and possibly the underlying companies.

To date, we have not conducted clinical trials on any pharmaceutical drugs and our platform has not been used to identify a drug candidate that has received regulatory approval for commercialization. However, we currently have a strategic relationship with a leading rare disease non-profit organization for AI/ML analysis of late-stage clinical data. We have acquired the rights to a series of preclinical and early clinical drug assets from universities and entered into a strategic collaboration with a world-renowned research institution to create a HSV1 viral therapeutic platform to engineer immunotherapies for colorectal cancer. We have signed exclusive worldwide license agreements with Johns Hopkins University for a cancer drug that targets glioblastoma (brain cancer), pancreatic cancer, and other cancers. We have also signed an exclusive worldwide license with George Washington University for another cancer drug that targets hepatocellular carcinoma (liver cancer), and other liver diseases.

Our platform was originally developed by JHU-APL. JHU-APL uses the same technology for applications related to national defense. Over several years, the software and algorithms have been used to identify relationships, patterns, and anomalies, and make predictions that otherwise may not be found. These discoveries and insights provide an advantage when predicting a target of interest, regardless of industry or sector. We have applied the technology to various clinical data sets and have identified novel relationships that may provide new intellectual property, new drug targets, and other valuable information that may help with patient stratification for a clinical trial thereby improving the odds for success. The platform has not yet aided in the development of a drug that has reached commercialization. However, we have licensed one drug candidate that has completed a Phase I trial and a second candidate that is in the preclinical stages. Our aim is to use our technology on current and future available data to help us better determine the optimal path for development.

While we have not generated significant revenues from our AI/ML operations, we anticipate generating revenue in the future from the following three sources:

Contract Services

Our fee for service partnership offering model is designed for biopharmaceutical companies, as well as other organizations, of all sizes that have challenges analyzing data throughout the drug development process. We provide the customer with an analysis of large complex data sets using our proprietary AI/ML platform called bfLEAP™. This platform is designed to predict targets of interest, patterns, relationships, and anomalies. Our service model involves fees in cash, equity or other consideration and, in some instances, the potential for rights to new intellectual property generated from the analysis, which can be performed at the discovery, preclinical, or clinical stages of drug development.

Collaborative Arrangements

We plan to enter into collaborative arrangements with biotechnology and pharmaceutical companies who have drugs that are in development or have failed late Phase II or Phase III trials. The collaborations may also be at the discovery or preclinical stages of drug development. Our revenue will be a combination of fee for service payments and success fees based on achieving certain milestones as determined by each specific arrangement. There may also be fees or legal rights associated with the development of new intellectual property.

Acquisition of Rights to Certain Drugs

We may acquire the rights to drugs that have failed late Phase II or Phase III trials and generate revenues by using our platform to accurately determine the profile of patients that would respond to the drugs, conduct a clinical trial to test our findings either independently or with a clinical partner, and finally sell the drug back to pharmaceutical companies. We have and may continue acquiring the rights to drugs that have not yet failed any trials. We will use our technology to improve the chances for success, conduct a trial, and divest the asset. When divesting assets, the transaction may involve a combination of upfront payments, milestone payments based on clinical success, and royalties on sales of the product.

Our Products

Product/Platform	Description	Target Market/Indications
bfLEAP™ – AI/ML platform for analysis of preclinical and clinical data	AI/ML analytics platform derived from technology developed at JHU-APL and licensed by the Company.	Biotechnology and pharmaceutical companies and other organizations.

siRNA	Small interfering RNA targeting Beta2-spectrin in the treatment of human diseases developed at George Washington University and licensed by the Company. Product has not yet initiated clinical testing.	Hepatocellular carcinoma, treatment of obesity, non-alcoholic fatty liver disease, and non-alcoholic steatohepatitis.

Mebendazole	Improved formulation of Mebendazole developed at Johns Hopkins University and licensed by the Company. Product has begun the process of clinical testing but has not received regulatory approval for commercialization.	Glioblastoma.

On January 14, 2022, the Company entered into an exclusive, worldwide, royalty-bearing license from George Washington University (“GWU”) for rights to use siRNA targeting Beta2-spectrin in the treatment of human diseases, including hepatocellular carcinoma (“HCC”). The license covers methods claimed in three U.S. and worldwide patent applications, and also includes use of this approach for treatment of obesity, non-alcoholic fatty liver disease, and non-alcoholic steatohepatitis. This program is currently in the preclinical stage of development. The Company initiated proof-of-concept studies on this asset and will use the outcome of these studies to inform a clinical development plan that would include initiation of IND-enabling studies.

Metabolic dysfunction-associated steatotic liver disease (known as MASLD, which until recently was called non-alcoholic fatty liver disease, or NAFLD) is a condition in which excess lipids, or fat, build up in the liver. This condition, which is more common in people who have obesity and related metabolic diseases including type 2 diabetes, affects as many as 24% of adults in the United States and is associated with risk of progression to more serious conditions, including metabolic dysfunction-associated steatohepatitis (“MASH”), with associated liver inflammation and fibrosis, and HCC. Evidence in animal models of obesity suggest that a protein called β2-spectrin may play a key role in lipid accumulation, tissue fibrosis, and liver damage, and targeting expression or activity of this protein may be a useful approach in treating MASH and liver cancer (Rao et al., 2021).

In February 2022, the Company entered into an exclusive, worldwide, royalty-bearing license with Johns Hopkins University (“JHU”) for the use of an improved formulation of Mebendazole for the treatment of any human cancer or neoplastic disease. This formulation shows potent activity in animal models with different types of cancer and has been evaluated in a Phase I clinical trial in patients with high-grade glioma (NCT01729260). The trial, an open-label dose-escalation study, assessed the safety of the improved formulation with adjuvant temozolomide in 24 patients with newly diagnosed gliomas. Investigators observed no dose-limiting toxicity in patients receiving all but the highest tested dose (200mg/kg/day). Four of the 15 patients receiving the maximum tested dose of 200mg/kg/day experienced dose-limiting toxicity, all of which were reversed by decreasing or eliminating the dose given. There were no serious adverse events attributed to mebendazole at any dose during the trial. The Company is currently formulating a strategy to find a partner to conduct additional clinical trials with this asset to enable evaluation of safety in humans.

In October 2022, the Company entered into an exclusive, world-wide, royalty-bearing license with JHU and the Institute of Organic Chemistry and Biochemistry (“IOCB”) of the Czech Academy of Sciences for rights to commercialize N-substituted prodrugs of mebendazole that demonstrate improved solubility and bioavailability. The license covers prodrug compositions and use for treating disease as claimed in multiple United States and worldwide patent applications. Patents have since been issued in the United States and Australia and are still in the prosecution phase in other territories. In September 2023, the Company announced results from a preclinical study demonstrating the effectiveness of BF-223, a compound chosen from this class, in an animal model for glioblastoma. The Company is currently formulating a strategy for initiating IND-enabling studies on BF-223 and is conducting outreach to identify partners that may want to license or partner in the development of BF-223.

Our bfLEAP™ Analytics Platform

We are able to pursue our drug rescue business by leveraging a powerful and proven AI/ML platform (trade name: bfLEAP™) derived from technology developed at JHU-APL. The bfLEAP™ platform is based on an exclusive, world-wide license granted by JHU-APL. The license covers three (3) issued patents, as well as a new provisional patent application, non-patent rights to proprietary libraries of algorithms and other trade secrets, which also includes modifications and improvements. On July 8, 2022, the Company entered into an exclusive, world-wide, royalty-bearing license from JHU-APL for the additional technology developed to enhance the bfLEAP™ platform. The new license provides additional intellectual property rights including patents, copyrights and knowhow to be utilized under the Company’s bfLEAP™ analytical AI/ML platform. Under the terms of the new license agreement, JHU-APL will be entitled to eight percent (8%) of net sales for the services provided by the Company to other parties and three percent (3%) for internally developed drug projects in which the JHU-APL license was utilized. The new license also contains tiered sub licensing fees that start at 50% and reduce to 25% based on revenues.

We believe the bfLEAP™ analytics platform is a potentially disruptive tool for analysis of pre-clinical and clinical data sets, such as the robust pre-clinical and clinical trial data sets being generated in translational R&D and clinical trial settings. The input data for bfLEAP™ can include raw data (preclinical and/or clinical readouts), categorical data, sociodemographic data of patients, and various other inputs. Thus, the bfLEAP™ platform is capable of capturing the particular genetic and physical characteristics of patients in an unbiased manner, and contextualizing it against other disparate data sources from patients (e.g. molecular data, physiological data, etc.) for less biased and more meaningful conclusions. It is also uniquely scalable - the bfLEAP™ platform is able to perform analysis on large, high-volume data sets (i.e. ‘big data’) and also able to analyze highly disparate “short and wide” data as well. In terms of visualization, bfLEAP™ is able to integrate with most commonly used visualization tools for graph analytics.

We believe that the combination of (a) scalable analytics (i.e., large data or short/wide data), (b) state-of-the-art proprietary algorithms, (c) unsupervised machine learning, and (d) streamlined data ingestion and visualization makes bfLEAP™ one of the most flexible and powerful new platforms available on the market.

The Company will continue to evolve and improve bfLEAP™.

Bullfrog Data Networks™

The Company’s Bullfrog Data Networks™ solution incorporates publicly available, proprietary, and custom data sources to generate novel insights toward target identification and validation, understanding mechanism of action, clinical trial optimization, drug repurposing, and more. Bullfrog Data Networks™ can be created for any therapeutic area, and we use this technology in our own research and development pipeline including in oncology and central nervous system indications. Our approach finds patient subgroups with similar molecular signatures and identifies the most relevant genes driving disease biology. Data networks created from this process generate insights that allow researchers to accelerate drug discovery and development and increase the odds of technical and regulatory success. In February 2025, we entered into a collaboration agreement with Eleison Pharmaceuticals Inc. (“Eleison”), a Phase III oncology company focused on novel chemotherapeutic treatments for rare cancers, where we will apply our proprietary Bullfrog Data Networks™ solution, powered by the bfLEAP® platform, and we plan to offer this solution to other biotechnology and pharmaceutical companies going forward.

Lieber Institute for Brain Development

On September 8, 2023, the Company entered a data use and technology partnership agreement (the “Partnership Agreement”) with the Lieber Institute for Brain Development (“LIBD”). The Partnership Agreement covers the right of the Company to leverage its bfLEAP™ platform to mine LIBD’s comprehensive brain data, including transcriptomic, genomic, DNA methylation, cell-line, clinical, and imaging data to identify previously unrecognized relationships. The goal of the partnership is to identify previously unrecognized relationships between genes and pathways in the brain and the development of neurologic and psychiatric disorders, thereby facilitating the development of more effective treatments for diseases of the human brain. The collaboration will proceed in two stages, with the first involving unsupervised construction of graphical models to reveal relationships between brain diseases and genomic/biologic attributes, with the goal of identifying new biomarkers and drug targets across disorders. The second stage will involve creating disease-specific models that will enable identification of genes and pathways within these respective disorders. The Partnership Agreement had a one-year term of data exclusivity to complete the first stages of analyses, with a two-year extension option as performance milestones are met.

As contemplated in the Partnership Agreement, on October 16, 2023, the Company and LIBD entered into a commercial agreement (the “Commercial Agreement”) that sets forth the key terms for commercialization of products and services developed under the Partnership Agreement. Pursuant to the Commercial Agreement, LIBD granted the Company a worldwide, royalty-bearing exclusive license so long as the Company receives net sales or income from the licensing of “Licensed Products” (as defined in the Commercial Agreement) in the application of machine learning and artificial intelligence for research and development in drug development, and specifically includes therapeutic products, patient selection strategies, and target identification, but excludes diagnostics and incidental uses of machine learning and/or artificial intelligence on data derived from research. Generally, “Licensed Products” are any product or service which incorporates, results from, or is derived from LIBD’s Data (meaning finished brain-related data, including but not limited to DNA methylation, RNAseq, genomic, DNA methylation, cell-line, clinical, and imaging data, and the specified data set forth in the Partnership Agreement) and that the Company or its affiliate develops during the term of the Partnership Agreement, and any improvements thereof after the term of the Partnership Agreement, and all Licensed Products or services derived therefrom by the Company or its affiliates. Licensed Products may include, but are not limited to, biomarker and target identification, target validation, mapping unmet needs, identifying genetic risk factors and predictive modeling.

The Company was also granted the right to sublicense, to use the deliverables under the Partnership Agreement, and LIBD’s intellectual property rights in the data, to (i) use, sell, distribute for sale, have distributed for sale, offer for sale, have sold, import and have imported Licensed Products and (ii) to develop, have developed, make, have made Licensed Products that are derived from Licensed Products developed during the term of the Partnership Agreement, and any improvements made following the term. The Company is prohibited from sublicensing LIBD Data. The Company shall pay LIBD a royalty based on net sales of all Licensed Products sold by the Company and its affiliates.

The Commercial Agreement, generally, may be terminated at any time by either the Company or LIBD if either party defaults or breaches any material term of the agreement or files for protection under bankruptcy laws, makes an assignment for the benefit of creditors, appoints or suffers appointment of a receiver, trustee, or similar agent over its property.

Summary for CATIE Schizophrenia Case Study

As part of the Partnership Agreement, the Company worked with LIBD to analyze data from the landmark Clinical Antipsychotic Trials of Intervention Effectiveness (“CATIE”) trials. The CATIE trials were the largest trials ever conducted for anti-psychotic medications. The Company analyzed CATIE data from ~200 schizophrenia patients, with a library of almost 1 million genetic data points for each patient, more than 200 non-genetic attributes per patient, and 4 different medications used in the trial. For each of the four medications used, bfLEAP™ analysis revealed new, previously unknown relationships between individual genetic variants and negative patient symptoms. The genetic loci identified represent potential druggable targets, as well as potential stratifying criteria for future clinical trials in schizophrenia.

We performed another analysis on the data using our new advanced clustering algorithms bfLEAP 2.0 but focused on one particular drug named Olanzapine. Our bfLEAP™ 2.0 analytical results identified previously unknown, multi-dimensional associations among newly identified genetic variants, drug clearance, clinical trial sites, and clinical outcome variables in schizophrenia patients.

FIGURE 1 – bfLEAP™ Analytical Map

Each green node represents a different sampling of the data, and arrows point to attributes (blue nodes) which were found to be key indicators according to that sampling. Attribute importance is determined by how many samplings identify that attribute as an indicator (i.e., number of incoming arrows to each blue node).

Identification of clustered multi-variate associations (e.g., novel genetic variants, drug clearance, substance abuse) could help us (1) identify novel drug targets, (2) predict which patients are most likely to respond, and (3) identify modifiable factors that could contribute to better outcomes.

Summary for Cardiovascular Case Study

The Company worked with an international collaborator in cardiovascular devices to analyze data from an ongoing clinical trial for a new device. Bullfrog analyzed data from ~55 patients, with a library of almost 15,000 unique attributes of data for each patient. The data also included adverse events, and key demographic information. For this collaborator, bfLEAP™ analysis was able to provide ground truth for the Company, confirming multiple correlations and non-correlations within the data. In terms of actionable output, the analytical results confirmed at least two demographic co-variates for the ongoing trial, and also provided a starting point for deeper physiological and molecular studies.

Our Supply Chain and Customer Base

We have launched our businesses using funds from our initial public offering and through our partnerships and relationships. We have a strategic relationship with FSHD Society, a leading non-governmental organization, for AI/ML analysis of clinical trial data for patients with a rare neuromuscular disorder. We also have several other developing strategic relationships in the project design phase. The Company has executed a joint development deal for a biologics discovery phase opportunity that is directed toward targeted cancer therapeutics. The Company has also obtained exclusive worldwide rights to a Phase II ready glioblastoma drug and a discovery phase hepatocellular carcinoma drug from universities. Since we intend to conduct late-stage clinical trials with partners on rescued therapeutic assets, there will be a requirement of drug product or other significant services to plan and execute our clinical development programs. The success of our partnered clinical development programs will require adequate availability of raw materials and drug product for our R&D and clinical trials, and, in some cases, may also require establishment of third-party arrangements to obtain finished drug product that is manufactured appropriately under industry-standard guidelines, and packaged for clinical use or sale. Since we are a digital biopharmaceutical company, our clinical development programs will also require, in some cases, the establishment of third-party relationships for execution and completion of clinical trials.

Our Market Opportunity

One aim of our business is to “rescue” drugs that have failed in Phase III clinical trials by using our technology to analyze all available data with the goal of designing a precision medicine clinical trial that will have a better chance of being successful. The graphic below illustrates the estimated market opportunity for these failed drugs. The top arrow shows the number of failed Phase III trials for several disease categories over a 5-year period. The arrows below provide our assumptions for narrowing or discounting certain parameters associated with the market size calculation. The final arrow shows the math behind the $47.1 billion market opportunity. To date, we have not penetrated the failed drug market, however; we are actively searching for failed drug opportunities.

SEC Form 10-K filed by Bullfrog AI Holdings Inc.