Item 1A. “Risk Factors,” Part II, Item 7. “Management’s Discussion and Analysis of Financial Condition and Results of Operations” and elsewhere in this Annual Report on Form 10-K. Given these uncertainties, you should not rely on these forward-looking statements as predictions of future events. The forward-looking statements contained in this Annual Report on Form 10-K are made as of the date of this Annual Report on Form 10-K, and we do not assume any obligation to update any forward-looking statements, whether as a result of new information, future events or otherwise, except as required by applicable law.

In addition, statements that “we believe” and similar statements reflect our beliefs and opinions on the relevant subject. These statements are based upon information available to us as of the date of this Annual Report on Form 10-K, and while we believe such information forms a reasonable basis for such statements, such information may be limited or incomplete. Our statements should not be read to indicate that we have conducted an exhaustive inquiry into, or review of, all potentially available relevant information. These statements are inherently uncertain and investors are cautioned not to unduly rely upon these statements.

Unless the context requires otherwise, references to the “Company,” “Lantern,” “we,” “us,” and “our” in this Annual Report on Form 10-K refer to Lantern Pharma Inc., a Delaware corporation, and, where appropriate, its wholly-owned subsidiaries.

RISK FACTOR SUMMARY

Our business is subject to numerous risks and uncertainties, including those described in Part I, Item 1A. “Risk Factors” in this Annual Report on Form 10-K. These risks include, but are not limited to the following:

PART I

Item 1. Business

Overview

We are a clinical stage biotechnology company, focused on leveraging artificial intelligence (“A.I.”), machine learning and genomic data to streamline the drug development process and to identify the patients that will benefit from our targeted oncology therapies. Our portfolio of therapies consists of small molecules that others have tried, but failed, to develop into an approved commercialized drug, as well as new compounds that we are developing with the assistance of our proprietary A.I. platform and our biomarker driven approach. Our A.I. platform, known as RADR^®, currently includes more than 25 billion data points, and uses big data analytics (combining molecular data, drug efficacy data, data from historical studies, data from scientific literature, phenotypic data from trials and publications, and mechanistic pathway data) and machine learning to rapidly uncover biologically relevant genomic signatures correlated to drug response, and then identify the cancer patients that we believe may benefit most from our compounds. This data-driven, genomically-targeted and biomarker-driven approach allows us to pursue a transformational drug development strategy that identifies, rescues or develops, and advances potential small molecule drug candidates at what we believe is a fraction of the time and cost associated with traditional cancer drug development.

Our strategy is to both develop new drug candidates using our RADR^® platform and other machine learning driven methodologies, and to pursue the development of drug candidates that have undergone previous clinical trial testing or that may have been halted in development or deprioritized because of insufficient clinical trial efficacy (i.e., a meaningful treatment benefit relevant for the disease or condition under study as measured against the comparator treatment used in the relevant clinical testing) or for strategic reasons by the owner or development team responsible for the compound. Importantly, these historical drug candidates appear to have been well-tolerated in many instances, and often have considerable data from previous toxicity, tolerability and ADME (absorption, distribution, metabolism, and excretion) studies that have been completed. Additionally, these drug candidates may also have a body of existing data supporting the potential mechanism(s) by which they achieve their intended biologic effect, but often require more targeted trials in a stratified group of patients to demonstrate statistically meaningful results. Our dual approach to both develop de-novo, biomarker-guided drug candidates and “rescue” historical drug candidates by leveraging A.I., recent advances in genomics, computational biology and cloud computing is emblematic of a new era in drug development that is being driven by data-intensive approaches meant to de-risk development and accelerate the clinical trial process. In this context, we intend to create a diverse portfolio of oncology drug candidates for further development towards regulatory and marketing approval with the objective of establishing a leading A.I.-driven, methodology for treating the right patient with the right oncology therapy.

A key component of our strategy is to target specific cancer patient populations and treatment indications identified by leveraging our RADR^® platform, a proprietary A.I. enabled engine created and owned by us. We believe the combination of our therapeutic area expertise, our A.I. expertise, and our ability to identify and develop promising drug candidates through our collaborative relationships with research institutions in selected areas of oncology gives us a significant competitive advantage. Our RADR^® platform was developed and refined over the last five years and integrates billions of data points immediately relevant for oncology drug development and patient response prediction using artificial intelligence and proprietary machine learning algorithms. By identifying clinical candidates, together with relevant genomic and phenotypic data, we believe our approach will help us design more efficient preclinical studies, and more targeted clinical trials, thereby accelerating our drug candidates’ time to approval and eventually to market. Although we have not yet applied for or received regulatory or marketing approval for any of our drug candidates, we believe our RADR^® platform has the ability to reduce the cost and time to bring drug candidates to specifically targeted patient groups. We believe we have developed a sustainable and scalable biopharma business model by combining a unique, oncology-focused big-data platform that leverages artificial intelligence along with active clinical and preclinical programs that are being advanced in targeted cancer therapeutic areas to address today’s treatment needs.

Scientific literature offers a definition for “drug rescue” as research involving abandoned small molecules and biologics that have not been approved by the U.S. Food and Drug Administration (“FDA”). These rescued molecular compounds are often abandoned by pharmaceutical companies in the drug discovery or preclinical testing phase, typically because they do not prove effective for the specific use for which they were developed. Some of these compounds may be useful in treating other diseases for which they have not been tested. See, Hemphill, Thomas A., “The NIH Promotes Drug Repurposing and Rescue,” Research Technology Management, v. 5, no. 5, pp. 6-8 (2012). Our use of the term “rescue”, “drug rescue”, or “drug rescuing” refers to, “…a system of developing new uses for chemical and biological entities that previously were investigated in clinical studies but not further developed or submitted for regulatory approval, or had to be removed from the market for safety reasons.”, which is a definition we believe is recognized in the drug discovery, drug development and pharmaceutical and biotechnology industries. See, Naylor, S. and Schonfeld J., “Therapeutic Drug Repurposing, Repositioning and Rescue,” DDW (Drug Discovery World) Winter 2014, and Mucke, HAM, A New Journal for the Drug Repurposing Community. Drug Repurposing, Rescue & Repositioning 1, 3-4 (2014). The use of the term “drug rescue,” “rescuing,” or words of similar meaning in this report should not be construed to mean that our RADR^® platform has resolved all issues of safety and/or efficacy for any of our drug candidates. Issues of safety and efficacy for any drug candidate may only be determined by the U.S. FDA or other applicable regulatory authorities in jurisdictions outside the United States.

Our current portfolio consists of four compounds and an Antibody Drug Conjugate (ADC) program: two drug candidates in clinical phases, two in the pre-IND preclinical stage and our ADC program in research optimization. All of these drug candidates and our ADC program are leveraging precision oncology, A. All of these drug candidates are leveraging precision oncology, A. I. and genomic driven approaches to accelerate and direct development efforts.

We currently have two drug candidates in clinical development, LP-100 and LP-300, where we are leveraging data from prior preclinical studies and clinical trials, along with insights generated from our A.I. platform, to target the types of tumors and patient groups we believe will be most responsive to the drug. platform, to target the types of tumors and patient groups that would be most responsive to the drug. Both LP-100 and LP-300 showed promise in important patient subgroups, but failed pivotal Phase III trials when the overall results did not meet the predefined clinical endpoints. We believe that this was due to a lack of biomarker-driven patient stratification. LP-300 has been studied in multiple randomized, controlled, multi-center non-small cell lung cancer, or NSCLC, trials that included administration of either paclitaxel and cisplatin and/or docetaxel and cisplatin, and we are currently conducting a targeted phase II trial (the Harmonic™ trial) for LP-300 in never smoking patients with NSCLC in combination with chemotherapy, under an existing investigational new drug application. LP-100 was previously out-licensed by us to Allarity Therapeutics A/S. In July 2021, we entered into an Asset Purchase Agreement to reacquire global development and commercialization rights for LP-100 from Allarity.

Additionally, we have two new drug candidates, LP-184 and LP-284, in pre-IND preclinical development for multiple potentially distinct indications where we are leveraging machine learning and genomic data to streamline the drug development process and to identify the patients and cancer subtypes that will best benefit from these drugs, if approved. Subject to regulatory clearance to move forward under future IND applications, we are planning a Phase I clinical trial for LP-184 to begin in mid 2023 and a Phase I clinical trial for LP-284 to begin in mid 2023. Our ADC program commenced in early 2021 is aimed at identifying targeted or therapeutic antibodies to conjugate with selected compounds. Subject to regulatory clearance to move forward under a future IND application, we are planning a Phase I clinical trial for LP-184 across multiple solid tumors that express a certain biomarker profile, and in glioblastoma to begin in late 2021 or early 2022. Our antibody drug conjugate (ADC) program is in early stage development and compound optimization for solid tumors. In January 2023, we formed a wholly owned subsidiary, Starlight Therapeutics Inc. (“Starlight”), to develop drug candidate LP-184’s central nervous system (CNS) and brain cancer indications – including glioblastoma (GBM), brain metastases (brain mets.), and several rare pediatric CNS cancers. Following the formation of Starlight, we will refer to the molecule LP-184, as it is developed in CNS indications, as “STAR-001”.

Our development strategy is to pursue an increasing number of oncology focused, molecularly targeted therapies where artificial intelligence and genomic data can help us provide biological insights, reduce the risk associated with development efforts and help clarify potential patient response. We plan on strategically evaluating these on a program-by-program basis as they advance into clinical development, either to be done entirely by us or with out-licensing partners to maximize the commercial opportunity and reduce the time it takes to bring the right drug to the right patient.

As part of our overall growth strategy, we plan to grow our pipeline by identifying new drug candidates and pursuing potential indications for LP-300, LP-184, LP-284, and LP-100 while leveraging our RADR^® platform. We are also pursuing the identification and design of potential combination therapies in cancer for our compounds by leveraging our RADR^® platform to analyze synergistic genomic networks and biological pathways with other currently approved drugs.

We have an extensive multi-national portfolio of intellectual property directed to our drug candidates, and to protect the targeted use and development of our portfolio of compounds in specific patient populations and in specific therapeutic indications. In addition, as our RADR^® platform and other machine learning driven methodologies progress and mature, we will continue to evaluate additional ways to further protect these assets.

As of March 1, 2023, we own or control over 80 active patents and patent applications across over 16 patent families whose claims are directed to our drug candidates and what we plan to do with our drug candidates. We have in-licensed or acquired patents and patent applications from AF Chemicals, and BioNumerik that are directed to the compounds, LP-100, LP-184, LP-284 and LP-300, and methods of using the compounds. We have in-licensed or acquired patents from AF Chemicals, and BioNumerik directed to the compounds, LP-100, LP-184 and LP-300. Additionally, we have also filed patent applications to further enhance and extend the use of these in-licensed compounds. Additionally, we have also filed patent applications to further enhance, and extend the use of these in-licensed compounds. Our 14 patent families are directed to our drug candidates, their usage, manufacturing and other matters. These matters are essential to precision oncology and relate to: (a) data-driven, biologically relevant biomarker signatures, (b) patient selection and stratification approaches that rely on prediction of response derived from these signatures and, (c) the ability to develop novel, combination therapy approaches with existing therapeutics. These matters are essential to precision oncology and relate to: (a) uniquely powerful, data-driven, biologically relevant biomarker signatures, (b) patient selection and stratification approaches that rely on prediction of response derived from these signatures and, (c) the ability to develop novel, combination therapy approaches with existing therapeutics.

Our Drug Candidate Pipeline

One of the ways we are building our drug candidate pipeline is by in-licensing clinical stage drug candidates that may have been discontinued for development. We use our RADR^® platform to assist in analyzing prior clinical research conducted by others to identify small-molecule oncology drug candidates that have (i) a well-tolerated profile evidenced by completion of phase I clinical trials, and (ii) demonstrated at least limited antitumor or anticancer activity in clinical trials. We intend to advance the drug candidates in our pipeline as potential precision medicine treatments for cancer. Our targeted development workflow includes preclinical studies where drug activity and associated gene signatures are identified, in part through strategic collaborations with some of the top academic institutions and clinical translational centers in the world. Using this collaborative approach, together with innovative observations from our RADR^® platform, we intend to develop and add drug candidates to our pipeline with the objective of treating the right patient populations with the right oncology therapies.

Our current pipeline of development programs involves four small molecule drug candidates: LP-300, LP-100, LP-184, and LP-284, and an Antibody Drug Conjugate (ADC) program.

We currently have an existing IND in the U.S. for LP-300 that was transferred to us as part of our in-licensing and agreement with BioNumerik to acquire the rights to the compound. There is currently no active IND in the U.S. for LP-100, LP-184 and LP-284.

Our Precision Cancer Therapy Development Using Our Innovative RADR^® Platform

RADR^® is one of the world’s largest A.I. and machine learning (M.L.) oncology drug discovery and development platforms, consisting of over 25+ billion oncology-focused data points. These data points consist of large-scale multi-omic data, derived from 130,000+ patient records, 150+ drug-tumor interactions, thousands of drug classes, and covering over 135 cancer subtypes. RADR^® leverages this data and over 200+ advanced ML algorithms to power its drug discovery and development modules. RADR^®’s data, capabilities, and insights have powered the development of new Lantern drug candidates, advancement of new indications for existing drugs, and identification of potential new drug combinations.

Historically, cancer treatment protocols include surgery, chemotherapy and radiation therapy. Treatments have been selected based on histologic type and disease spread, irrespective of genetic differences among patients. With the advent of precision therapies, cancer treatments increasingly target specific genes or mechanisms of action for a more personalized approach to patient care. This trend represents a substantial advance in cancer treatment because tumor growth is highly dependent on genetic changes and the genetic profile of the individual and the progression of the disease is highly variable amongst patients.

Our RADR^® platform is core to our drug development approach for identifying the desired candidates to in-license and develop. According to a recent article in JAMA (Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-18, JAMA, March 3, 2020) oncology drug development is costly, risky, and highly competitive with an average success rate of 4% to 8% and average developmental costs of over $1 billion per successful drug. There is a critical need to rescue clinical research on drugs that have failed clinical trials in order to provide additional possible therapies for patients while reducing the overall cost of therapeutic development. Many drug failures within oncology may be attributed to the heterogeneity of the tested patient population, even though there may be a strongly positive therapeutic impact on certain patient subgroups within that population.

As data-centric and machine learning approaches begin to change the pace and scale of drug discovery and development, research and development (“R&D”) we believe efforts in large biopharma companies will begin to shift away from traditional approaches towards new data and A.I.-centric approaches. According to Deloitte Consulting, in Ten Years On | Measuring the return from pharmaceutical innovation 2019, “decades of advances in science and technology have driven improvements in health care outcomes and influenced stakeholder expectations of the role of the biopharmaceutical industry (biopharma). However, the past decade has seen increasing pressures undermine the productivity of biopharma R&D, leading to multiple years of decline in the return on investment. At the same time, innovative new treatments are changing the face of disease management. New treatment modalities and an increasing understanding of precision medicine have led to the need for new R&D models...” The Deloitte Consulting report further describes that R&D costs will, “shift from traditional discovery and trial execution to a process driven by large datasets, advanced computing power and cloud storage”.

Analysts estimate that this shift from traditional screening, and trial-based studies to leveraging in silico, data and A.I. methodologies has driven a significant increase in the spending on A. methodologies will drive a significant increase in the spending on A. I. by the biopharma and drug discovery community to approximately $4 billion in 2021, increasing by about 40% annually from $730 million in 2019 according to PMLive and Global Market Insights. As a result of these trends and changes in the R&D model in biopharma, we believe that we, and companies that are using data-centric and A.I. centric approaches to drug discovery and development, are in an ideal position to benefit from this industry shift that has the potential to help deliver drugs to the right patients faster, with a higher degree of personalization and a potentially lower amount of average costs in the development cycle.

Our drug rescue approach leverages substantial prior research and development investments in candidates that were withdrawn from development prior to submission for FDA approval. The large volume of failed compounds, recent developments that permit increased access to validated genomic and biomarker data, and the rapid evolution of A. The large volume of failed compounds, recent developments that permit increased access to validated genomic and biomarker data, and the rapid evolution of AI technology creates an opportunity to efficiently capitalize on these investments. I. technology creates an opportunity to efficiently capitalize on these investments.

Our RADR^® platform is rapidly emerging as a robust and scalable platform for targeted cancer therapy development. Through the use of A.I. and machine learning, RADR^® is designed to quickly identify and guide the development of compounds that we can develop as potential oncology agents through either a process of drug rescue, drug repositioning or de-novo development. RADR^® is being developed through an accumulation and curation of genomic and biomarker data that is directly relevant to the measurement and classification drug-tumor interaction, and clinical datapoints related to patient response and patient stratification.

Predicting optimal drug responses in cancer patients requires the identification and validation of predictive biomarkers. Our RADR^® platform seeks to identify biomarkers to assist in selecting patients who have the highest likelihood to respond to our drug candidates. For example, the targeted indications for our drug candidate LP-184 were chosen in part because they are known to highly express the protein coding gene PTGR1. Our planned clinical trial for LP-184 is intended to provide additional information regarding biomarkers related to LP-184’s molecular and cellular targets. For example, the targeted indications for our drug candidate LP-184 were chosen in part because they are known to highly express the protein coding gene PTGR1. Our preclinical “PRostate cancer Artificial Intelligence Study using Ex vivo models” or “PRAISE” trial and our planned clinical trial for LP-184 are intended to examine biomarkers related to LP-184’s molecular and cellular targets to identify those that may correlate with clinical observed anticancer activity. This method of using and validating targeted biomarkers during development and then using these biomarkers during clinical trials can lead to shortening of the development timeline and compression of costs associated with oncology drug development.

Similarly, we believe LP-300 targets molecular pathways that are more common in never smokers than in other groups and also targets kinases involved in key signaling pathways involving enzymes critical for DNA synthesis and repair, such as Excision Repair Cross-Complementation Group 1 (ERCC1), Ribonucleotide Reductase 1 (RNR1), Ribonucleotide Reductase 2 (RNR2), as well as enzymes and proteins important in regulating cell redox status, such as Thioredoxin (TRX), Peroxiredoxin (PRX), Glutaredoxin (GRX), and Protein Disulfide Isomerase (PDI).

Our RADR^® Platform

The human genome consists of 19,000 to 20,000 protein coding genes. One input record derived from available data bases and analyzed by our RADR^® platform consists of datapoints (expression values) from approximately 20,000 genes, another input record type is drug sensitivity data (IC20, IC50), and other sets include key clinical parameters from HIPAA compliant patient data and clinical histories. Our RADR^® platform uses a data-driven gene feature selection methodology that is a combination of biology, informatics, and statistics – computational biology. The architecture, tools and software of our platform are depicted in the figures below. The architecture and modules of our platform are depicted in the image below.

We developed our platform using primarily open-source third party supervised algorithms such as Neural Networks, Support Vector Machine, Random Forest, K-Nearest Neighbors, Logistic Regression and Penalized Multivariate Regression. Each algorithm is trained with input data to predict drug sensitivity (regressor models) and stratify patient response as responder or non-responder (classifier models). Model tuning and optimization is then performed using a hyperparameter search algorithm in order to produce the predicted lowest cross validation error. The models are then evaluated using traditional performance metrics such as accuracy, area under the curve, sensitivity, specificity, precision, root mean square error and mean absolute error calculations.

A feature reduction algorithm is then used to reduce the number of genes under analysis to a biomarker gene panel of less than approximately 50 genes. This set of genes is intended to carry the highest coefficient to predict drug sensitivity and the highest variable importance in classifying a responder from a non-responder. Genes that do not help in predicting the output variable are eliminated sequentially.

Our RADR^® Platform Workflow

Our RADR^® platform’s proprietary workflow involves preliminary statistical analysis on approximately 18,000 features typically from whole transcriptomic datasets reducing the set to approximately 2,000 features. This is followed by gene filtering via biological and statistical methodologies yielding approximately 200 significant genes. The platform currently contains 6 feature selection methods and 13 machine learning methods to analyze the drug and omics data, in order to fine tune the model and get better and improved prediction accuracy. Feature selection ensures that genes that do not contribute to response prediction are excluded from the output dataset. The prediction component subsequently applies an A.I.-driven reduction algorithm to the previously filtered genes generating a targeted set of typically less than 50 candidate biomarkers predictive of response to a particular molecule. The figure below illustrates RADR^®’s workflow.

A distinct and unique benefit of the RADR^® platform is its ability to integrate biological knowledge and data-driven feature selection to generate hypothesis-free biomarker signatures. This can then aid in identifying novel targets for predictive screening and drug development.

Our RADR^® platform is enabled through access to, and analysis of, a number of key datasets: (i) publicly available databases (ii) data from commercial clinical studies and trials and (iii) our proprietary data generated from ex vivo 3D tumor models specific to drug-tumor interactions. We incorporate automated supervised machine learning strategies along with big data analytics, statistics and systems biology to facilitate identification of new correlations of genetic biomarkers with drug activity.

The value of the platform architecture is derived from its validation through the analysis of over 25 billion oncology-specific clinical and preclinical data points, more than 154 drug-cancer interactions, thousands of drug classes, data covering more than 135 cancer subtypes, and over 130,000 patient records from 16 databases, one of which is our internal database. RADR^® leverages this data and over 200+ advanced ML algorithms to power its drug discovery and development modules. Our long-term objective is to collect and analyze over 100 billion oncology-specific clinical and preclinical data points to further enhance the prediction power of our RADR^® platform. We use cancer cell line gene expression profiles and drug sensitivity data (IC50) as one of its input types. In a population of 10 case studies our platform was able to distinguish responders from non-responders with an average historical accuracy of over 80%. We have also used our platform to generate genetic signatures that we believe to have applicability for the majority of FDA approved drug-tumor indications. External validation, through retrospective data analysis, of patient datasets from 10 independent clinical studies achieved an average response prediction accuracy greater than 80%, and internal analysis of 120 drug-tumor interactions in cell lines achieved an accuracy of greater than 85%. The figure below illustrates examples of RADR^®’s algorithms and how they can be used.

We have developed our platform in a cloud environment that efficiently uses parallel processing to analyze patient stratification and biomarker selection. Best software engineering practices are followed while designing and developing our platform’s architecture. In order to track modifications in the software, a version control system is in place. We use a software release process, including a rigorous regression testing process, to ensure functions and programs are working as designed.

Our platform uses a simple user input and GUI based AI architecture that can be used in many pharmaceutical research areas such as biomarker identification, patient stratification, drug rescue and reposition by bioinformaticians, clinicians and trained wet-lab scientists.

In late 2021, the Code Ocean Platform, a secure cloud-based computing environment manager, was integrated into RADR^®. The Code Ocean environment has upgraded RADR^®’s data organization, synchronization, scalability and accessibility. These architecture changes have enhanced the reproducibility of RADR^® aided insights and analysis and created an environment that improves the ability to collaborate and share insights within Lantern and with Lantern’s collaborators. The figure below illustrates ways that RADR^®’s modules can be used to facilitate drug discovery and development within Lantern and with our collaborators.

Actuate Therapeutics Collaboration Utilizing RADR Platform

In May 2021, we entered into a Collaboration Agreement with Actuate Therapeutics, Inc. (“Actuate”), a clinical stage private biopharmaceutical company focused on the development of compounds for use in the treatment of cancer, and inflammatory diseases leading to fibrosis. Pursuant to the agreement, as amended, we are collaborating with Actuate on utilization of our RADR® platform to develop novel biomarker derived signatures for use with one of Actuate’s product candidates. As part of the collaboration, we received 25,000 restricted shares of Actuate stock subject to meeting certain conditions of the collaboration, as well as the potential to receive additional Actuate stock if results from the collaboration are utilized in future development efforts.

TTC Oncology Collaboration to Expand the Clinical Development of Drug Candidate TTC-352

In February 2023, we entered into a Collaboration Agreement with TTC Oncology (“TTC”). The collaboration is focused on using RADR^® to accelerate and sharpen the drug development of TTC’s Phase 2 ready drug candidate TTC-352. TTC-352, is a novel, first- and best-in-class selective human estrogen receptor (ER) partial agonist (ShERPA) for the treatment of patients with metastatic ER+ breast cancer. TTC-352 was recently evaluated in a Phase 1 accelerated dose escalation study for hormone receptor positive metastatic breast cancer, and it showed early efficacy signals in heavily pretreated hormone refractory patients. The initial aims of the collaboration are to 1) identify biomarker or gene signatures to power potential patient selection for an upcoming TTC-352 Phase 2 clinical trial, 2) further characterize TTC-352’s mechanism of action, and 3) discover additional treatment indications for TTC-352. Under the terms of the collaboration, Lantern is receiving an exclusive right to license TTC-352, including any collaboration intellectual property (“IP”), during an exclusive option period. Additionally, Lantern and TTC will each participate in upfront, milestone, and royalty payments in the event a third-party licenses IP resulting from the collaboration.

Our Strategy

Our mission is to bring the right cancer drugs to the right patients by transforming the drug development process through the use of artificial intelligence and data-driven development approaches. Our proprietary A.I.-enabled, and precision oncology approach, which focuses on developing our own pipeline of compounds by rescuing drug candidates that have previously failed and developing new compounds that are targeted to specific biological activity and genomic pathways, has the potential, we believe, to bring drugs to market faster, with lower costs, and with reduced risk, thereby enabling a change in the cost and availability of precision cancer therapy. We work with leading research laboratories, translational medicine and cancer centers to develop our studies and clinical trials for our portfolio, and actively update and improve our RADR^® platform to incorporate additional biomarker data, patient outcome data, cancer drug efficacy studies and computational models that relate to oncology drug development and prediction of patient response.

As part of our growth strategy, we plan to:

LP-300

General Overview

We are currently advancing LP-300 in a Phase II clinical trial (the “HARMONIC™ Study”) of LP-300 in combination with carboplatin and pemetrexed in never smoker patients with relapsed advanced primary adenocarcinoma of the lung after treatment with tyrosine kinase inhibitors (TKIs).

LP-300 is a cysteine-modifying molecular entity that works to modulate multiple cellular pathways simultaneously and is a potential combination agent for targeted indications in NSCLC. LP-300 is a small molecule (molecular weight 326.4 Da) that was in-licensed from BioNumerik Pharmaceuticals, Inc. in May 2016, and subsequently acquired by us in 2018. We are focused on repositioning LP-300 as a potential combination therapy for never smokers NSCLC patients with histologically defined adenocarcinoma. in May 2016, and subsequently acquired by us in 2018. We are focused on repositioning LP-300 as a potential combination therapy for non-smoking (or never-smoker) NSCLC patients with histologically defined adenocarcinoma. Prior clinical trials conducted by BioNumerik for LP-300 did not meet their primary clinical endpoints, and at least one or more future clinical trials that meet their pre-specified primary endpoints with statistical significance will be required before we can obtain a regulatory marketing approval, if any, to commercialize LP-300. Safety and efficacy determinations are solely within the authority of the FDA in the U.S. or other regulatory agencies in other jurisdictions. Currently there is no approved therapy specifically for the growing indication of never-smokers with NSCLC, and female never smokers appear to be uniquely responsive to LP-300. With both chemosensitizing and chemoprotective activity, LP-300 has potential as a combination agent or adjuvant in front line, second line or salvage therapy in newly diagnosed, relapsed, metastatic or advanced NSCLC for overall survival enhancement and toxicity alleviation from primary chemotherapy or standard of care. Currently there is no approved therapy specifically for the growing indication of non-smokers (or never-smokers) with NSCLC, and female non- or never smokers appear to be uniquely responsive to LP-300. With both chemosensitizing and chemoprotective activity, LP-300 has potential as a combination agent or adjuvant in front line, second line or salvage therapy in newly diagnosed, relapsed, metastatic or advanced NSCLC for overall survival enhancement and toxicity alleviation from primary chemotherapy or standard of care. We are currently in the early stages of defining a specific biomarker signature that correlates with heightened sensitivity to LP-300. We believe that this signature may help accelerate the clinical development of LP-300 and has the potential to guide patient selection for targeted clinical trials.

Prior clinical trials conducted by BioNumerik for LP-300 did not meet their primary clinical endpoints and at least one or more future clinical trials that meet their pre-specified primary endpoints with statistical significance will be required before we can obtain a regulatory marketing approval, if any, to commercialize LP-300. Prior clinical trial observations are not necessarily predictive of the outcome of any future clinical trials we may conduct.

LP-300 has been administered in multiple clinical trials to more than 1,000 subjects and has been generally well-tolerated. Retrospective analyses of the results of a multi-country phase III lung cancer trial (study ID DMS32212R) in subgroups of adenocarcinoma patients receiving LP-300, paclitaxel and cisplatin demonstrated substantial improvement in overall survival, particularly among female never smokers, where a 13.6 month improvement in overall survival (p-value 0.0167, hazard ratio 0.367) in favor of LP-300 was observed, as compared to placebo in the subgroup of paclitaxel/cisplatin-treated patients. Retrospective analyses of the results of a multi-country phase III lung cancer trial (study ID DMS32212R) in subgroups of adenocarcinoma patients receiving LP-300, paclitaxel and cisplatin demonstrated substantial improvement in overall survival, particularly among female never smokers, where a 13.6 month improvement in overall survival (p-value 0.0167, hazard ratio 0.367) in favor of LP-300 was observed, as compared to placebo in the subgroup of paclitaxel/cisplatin-treated patients. Similar retrospective findings of increased overall survival in the subgroup of LP-300/paclitaxel/cisplatin treated female Asian patients with adenocarcinoma of the lung were observed in a randomized, double-blind, placebo-controlled trial in Japan. Prior historical clinical trial observations are not necessarily predictive of the outcome of future trials. No assurances can be given that we will be successful in obtaining marketing approval for LP-300. The chemical structure of LP-300 is depicted below.

LP-300 Chemical Structure

LP-300 Phase II Clinical Trial

We are conducting a Phase II clinical trial (the “HARMONIC™ Study”) of LP-300 in combination with carboplatin and pemetrexed in never smoker patients with relapsed advanced primary adenocarcinoma of the lung after treatment with tyrosine kinase inhibitors. Our purpose in conducting the study is to determine the potential clinical advantages for this drug combination in the study-defined patient population. As of the date of this report, we have activated 5 clinical trial sites in the US, across 12 locations, and we anticipate multiple additional sites in the US during the first half of 2023, with first enrolled patients anticipated in the second quarter of 2023.

The trial is designed as a multicenter, open label, Phase II trial with planned enrollment of approximately 90 patients. Patients who are never smokers with lung adenocarcinoma and have relapsed after prior treatment with tyrosine kinase inhibitors will be eligible for enrollment. Following a six-patient safety lead-in stage, the trial consists of randomization in a 2:1 allocation ratio to one of two arms: Arm A (consisting of carboplatin, pemetrexed, and LP-300) or Arm B (consisting of carboplatin and pemetrexed).

The primary objective of this study is to determine progression-free survival and overall survival in the study-defined patient population when co-administered LP-300 with combination chemotherapy (carboplatin and pemetrexed) versus carboplatin and pemetrexed alone. The secondary objectives of the study are to evaluate tumor response measured by objective response rate, duration of objective response, and clinical benefit rate. We will also determine any associations between the efficacy endpoints and patient biomarkers (e.g., circulating tumor DNA and tumor genome characteristics) as an exploratory objective. Other exploratory objectives for the study may include evaluating quality of life in all patients and performance of patients based on the type, duration, and number of tyrosine kinase inhibitors received.

Key Findings from Prior LP-300 Clinical Trials

Summarized below are some key findings from LP-300’s prior clinical trials:

Background-Scope of Prior Phase III NSCLC Adenocarcinoma Trial (LP-300)

LP-300 was studied in a randomized, multi-center (trial locations in four US states and five European countries), double-blind and placebo-controlled Phase III trial from 2010 to 2013 in patients with adenocarcinoma of the lung (the “Phase III NSCLC adenocarcinoma trial”). The aim of the trial was to determine whether LP-300, combined with a standard combination of chemotherapy drugs, would increase survival in patients with advanced NSCLC adenocarcinoma. The secondary aim of the trial was to determine if the chemoprotective properties of LP-300 were effective in preventing or reducing common side-effects of cancer treatment, including kidney damage, anemia, nausea and vomiting that can occur with these drug combinations. The trial enrolled NSCLC patients with newly diagnosed or recurrent advanced (stage IIIB/IV) primary adenocarcinoma of the lung. Patients with confirmed histopathological diagnosis of inoperable and measurable advanced primary adenocarcinoma (including bronchioalveolar cell carcinoma) of the lung, and no prior systemic treatment for NSCLC including chemotherapy, immunotherapy, hormonal therapy, targeted therapies or investigational drugs, were included in the trial. Overall survival was the primary outcome measure. Patients in the control arm received standard of care (cisplatin and either paclitaxel or docetaxel) plus placebo, whereas patients in the treatment arm received standard of care (cisplatin and either paclitaxel or docetaxel) plus LP-300. The primary results of the trial for patients receiving cisplatin and paclitaxel are outlined in the table below. While the overall results of the Phase III NSCLC adenocarcinoma trial did not meet the specified endpoint of the trial in increasing overall survival in all patients, when the data were retrospectively separated by gender and smoking status, the trial data demonstrated that all never smokers, especially female never smokers, saw increased survival with LP-300 combination treatment with paclitaxel and cisplatin. Furthermore, the LP-300 group in the phase III NSCLC adenocarcinoma trial exhibited well-tolerated advantages relating to the potential to protect against chemotherapy-induced nephrotoxicity, neuropathy and nausea along with reduced anemia.

The figure below depicts the survival curves for cisplatin/paclitaxel subgroups for the Phase III NSCLC adenocarcinoma trial that ended in 2013, as summarized. The Kaplan Meier curves maintain consistent separation between treatment arms for the never smokers, females, and female never smokers.

Rationale Behind LP-300 Rescue and Repositioning Efforts

Based on the results from the prior Phase III NSCL adenocarcinoma trial, we have launched the HARMONIC™ LP-300 Phase II clinical trial to target the subpopulation of never smokers with adenocarcinoma that saw strong benefit in the previous Phase III trial. Although the incidence of never-smokers with NSCLC is rising currently there is no approved therapy specifically for the growing indication of never-smokers with NSCLC. Although the incidence of non-smokers with NSCLC is rising currently there is no approved therapy specifically for the growing indication of non-smokers (or never-smokers) with NSCLC. Preclinical observations support that LP-300 preferentially modulates ALK and EGFR, two commonly mutated genes in non-smokers with adenocarcinoma. Based on the findings from the previous Phase III NSCL adenocarcinoma trial, it is possible that the benefits of combining LP-300 with standard of care chemotherapy could be further improved by identifying additional molecular biomarkers in patients who respond well to LP-300 combination treatment. We continue to seek additional opportunities for LP-300. Some of our considerations include a never smoker population with a specific genetic signature that correlates to increased LP-300 sensitivity. We continue to seek additional opportunities for LP-300. Some of our considerations include a non- or never smoker population with a specific genetic signature that correlates to increased LP-300 sensitivity.

Disease Background and Opportunity

Lung cancer remains one of the most common and deadly cancers worldwide. Lung cancer accounts for 12% of all new cancer diagnoses, but 21% of all cancer deaths in the US. Lung cancer accounts for 13% of all new cancer diagnoses but 24% of all cancer deaths. Lung cancer kills more people annually than cancers of the breast, prostate, colon, liver, kidney, pancreatic, and melanoma combined. The American Cancer Society’s estimates for lung cancer in the US for 2023 are:

The most common type of lung cancer is called non-small cell lung cancer (“NSCLC”), which represents about 80% to 85% of all lung cancer.

Lung adenocarcinoma, a histological subtype of NSCLC that originates within the glands that line the lung, is the most common subtype of lung cancer in the world inflicting approximately 50% to 65% of non-Asians and approximately 70% to 85% of Asians diagnosed with lung cancer. According to LUNGevity Foundation, the National Institutes of Health and other published literature, 60% to 65% of all new lung cancer diagnoses are among people who are former smokers or have never smoked, while 10-15% of new lung cancer cases are among never-smokers.

Over one-half of the patients diagnosed with NSCLC in any given year will present with inoperable advanced (stage IV) disease, for which there is no cure. Patients with stage IV NSCLC exhibit a median overall survival time of 7 to 12 months; approximately one-third of patients will survive for a year, and only 10% to 21% of those patients will survive for two years.

Lung cancer is the most common cause of global cancer-related mortality, leading to over a million deaths each year and adenocarcinoma is its most common histological subtype. Worldwide, lung cancer occurred in approximately 2.2 million patients in 2020 and caused an estimated 1.8 million deaths. NSCLC is described as any type of epithelial lung cancer other than small cell lung cancer (“SCLC”). The 5-year survival rate for NSCLC is 25%.

Rapid advances in understanding the molecular pathogenesis of NSCLC have demonstrated that NSCLC is a heterogeneous group of diseases. Although the initial treatment of localized disease is the same, the molecular characterization of tumor tissue in patients with NSCLC serves as a guide to treatment both in those who present with metastatic disease and in those who relapse after primary therapy. Molecularly targeted therapies have dramatically improved treatment for patients whose tumors harbor somatically activated oncogenes such as mutant EGFR1 or translocated ALK, RET, or ROS1. Smoking is the major cause of lung adenocarcinoma but, as smoking rates decrease, proportionally more cases occur in never-smokers (defined as less than 100 cigarettes in a lifetime). Molecularly targeted therapies have dramatically improved treatment for patients whose tumors harbor somatically activated oncogenes such as mutant EGFR1 or translocated ALK, RET, or ROS1. Mutant BRAF and ERBB2 are also investigational targets. KRAS mutations in lung cancer cases are nearly exclusive to smokers. KRAS, “Kristen rat sarcoma viral oncogene homolog,” is a protein involved in regulating cell division. KRAS mutation is a gain-of-function mutation (i.e. somatic mutation turns RAS, a benign gene “proto-oncogene” into KRAS, an oncogenic driver of many tumors). KRAS-mutated non-small cell lung cancer represents 20% to 25% of all NSCLC. FDA granted accelerated approval to KRAS inhibitor sotorasib and Antibody Drug Conjugate trastuzumab deruxtecan (Enhertu) for KRAS G12C -mutated and HER2 mutated advanced stages non-small cell lung cancer (NSCLC), respectively. In 2022, the combination of CTLA-4 inhibitor tremelimumab and the anti-PDL1 antibody durvalumab was approved by FDA for treating metastatic NSCLC patients lacking EGFR mutation or ALK translocation. Tumor suppressor gene abnormalities, such as those in TP53, CDKN2A8, KEAP1, and SMARCA4 are also common but are not currently clinically actionable. Tumor suppressor gene abnormalities, such as those in TP53, STK11, CDKN2A8, KEAP1, and SMARCA4 are also common but are not currently clinically actionable.

In reviewing lung cancer incidence and mortality rates among never-smokers in the Journal of Clinical Oncology, Wakelee, H.A. et al. have reported that the age-adjusted incidence rates of lung cancer among never-smokers aged 40 to 79 years from large population-based cohorts ranged from 14.4 to 20.8 per 100,000 person-years in women and 4.8 to 13.7 per 100,000 person-years in men, supporting earlier observations that women are more likely than men to have never smoking-associated lung cancer. The biology of lung cancer in never-smokers is apparent in differential responses to epidermal growth factor receptor inhibitors and an increased prevalence of adenocarcinoma histology in never-smokers. Lung cancer in never-smokers is an important public health issue needing further exploration of its incidence patterns, etiology, and biology. Due to the fact that there are no known therapy options for this group, we believe that aggressive development of therapy options is needed and is a high unmet clinical need.

In the US in 2023, there will be an estimated 12,000 diagnosed cases of NSCLC in female non-smokers, accounting for approximately 5% of all lung cancer cases. Globally in 2020, there were an estimated 111,583 adenocarcinoma cases of NSCLC in female non-smokers. Due to the specificity of this indication, it may be possible to classify it as a rare disease. When attempting to explain some gender susceptibility differences, research has demonstrated that women with NSCLC tend to be:

The high rate of adenocarcinomas in non-smoking women suggests the possible existence of other etiological factors in addition to smoking. Some factors that have been considered include gender-specific genetic alterations and predispositions, passive smoke effects, different nicotine metabolism in women, occupational exposure, diet, and chronic obstructive pulmonary disease. Based upon estimates published by Global Cancer Statistics 2020 and 2023 estimates published by the American Cancer Society, below is an overview of relevant potential patient population and market sizes that we believe LP-300 could address, if approved:

Limitations on Current Treatment

Treatment of patients with advanced NSCLC in the first-line setting usually includes chemotherapy (including taxanes, vinorelbine, or gemcitabine) in combination with a platinum doublet (cisplatin or carboplatin). According to the clinical practice guidelines published by the National Comprehensive Cancer Network, many of these combinations have reached a plateau in terms of overall response (≥ 25% to 35%), time to progression (four to six months), median survival time (eight to ten months), one-year survival rate (30% to 40%), and two-year survival rate (10% to 15%) in patients with good performance status. Treatment remains palliative and is limited due to inherent toxicities that may affect the quality of life resulting from treatment. Toxicities can be life-threatening or cause treatment delays, thereby limiting the intensity of treatment delivered and affecting its efficacy. Common and serious chemotherapy-induced toxicities, such as anemia, emesis, and peripheral neurotoxicity resulting from treatment with platinum and taxanes, and nephrotoxicity due to cisplatin can result in treatment delays, dose modifications, and in severe cases, discontinuation of treatment.

The identification of gene mutations in lung cancer has led to the development of molecularly targeted therapy to improve the survival of subsets of patients with metastatic disease. In particular, genetic abnormalities in EGFR, MAPK, and PI3K signaling pathways in subsets of NSCLC may define mechanisms of drug sensitivity and primary or acquired resistance to tyrosine kinase inhibitors (TKIs). To date, approximately 21 TKIs have been approved for use in treating NSCLC with identified tyrosine kinase (TK) mutations; the TKs targeted by these inhibitors include EGFR, ALK, ROS1, BRAF/MEK, RET, and MET. If patients are found to have specific TK mutations to which inhibitors are known to respond, treatment with such TKIs is currently standard-of-care for this population of advanced NSCLC. Most tumors will respond to initial treatment with TKIs, exhibiting tumor shrinking or delayed progression. Unfortunately, most patients will eventually develop resistance to the inhibitory effects of initial used inhibitors. Therefore, second- or third-line therapy often involves treatment with alternate inhibitors targeting the same kinase but with differing mutations. Such treatment again is often initially successful, but further kinase mutations, or mutations arising in different kinases, often leads to relapse and the need to switch to alternative treatment schemes. This next therapy usually involves chemotherapy (often carboplatin plus pemetrexed), sometimes used in combination with immunotherapy, or enrollment in clinical trials testing new treatment approaches.