LTRN US Equity☆

Item 1A. Risk Factors 56

Item 1B. Unresolved Staff Comments. 102

Item 2. Properties. 102

Item 3. Legal Proceedings. 102

Item 4. Mine Safety Disclosures. 102

PART II

Item 6. Reserved. 105

Item 7A. Quantitative and Qualitative Disclosures About Market Risk. 113

Item 8. Financial Statements and Supplementary Data. F-1

Item 9A. Controls and Procedures. 114

Item 9B. Other Information. 114

PART III

Item 10. Directors, Executive Officers and Corporate Governance. 115

Item 11. Executive Compensation. 115

Item 14. Principal Accountant Fees and Services. 115

PART IV

Item 15. Exhibit and Financial Statement Schedules. 116

i

SPECIAL NOTE CONCERNING FORWARD-LOOKING STATEMENTS

This Annual Report on Form 10-K contains forward-looking

statements that involve substantial risks and uncertainties. We make such forward-looking statements pursuant to the safe harbor provisions

of the U.S. Private Securities Litigation Reform Act, Section 21E of the Securities Exchange Act of 1934, as amended, and other federal

securities laws. All statements, other than statements of historical fact, contained in this Annual Report on Form 10-K, including statements

regarding our strategy, future preclinical studies and clinical trials, future financial position, projected costs, prospects, plans and

objectives of management, are forward-looking statements. The words “anticipate,” “believe,” “contemplate,”

“could,” “estimate,” “expect,” “intend,” “seek,” “may,” “might,”

“plan,” “potential,” “predict,” “project,” “target,” "objective,"

“aim,” "upcoming," “should,” ‘will” “would,” or the negative of these words

or other similar expressions are intended to identify forward-looking statements, although not all forward-looking statements

contain these words. Forward-looking statements reflect our current views with respect to future events and are based on assumptions and

subject to risks and uncertainties.

The forward-looking statements

in this Annual Report on Form 10-K include, among other things, statements relating to:

● our strategic plans to advance the development of any of our drug candidates;

● our expectations related to future expenses and expenditures;

● the potential impact that COVID-19 may have on our business plans;

ii

We may not actually achieve

the plans, intentions, or expectations disclosed in our forward-looking statements, and you should not place undue reliance on our forward-looking

statements. Actual results or events could differ materially from the plans, intentions, and expectations disclosed in the forward-looking statements

we make. Factors that may cause actual results or events to differ materially from current plans, intentions, and expectations include,

among other things:

These factors could cause

actual results or events to differ materially from the forward-statements that we make. Furthermore, we operate in a competitive and rapidly

changing environment. New risks and uncertainties emerge from time to time, and it is not possible for us to predict all risks and uncertainties

that could have an impact on the forward-looking statements contained in this Annual Report on Form 10-K.

You should read this Annual

Report on Form 10-K and the documents that we file with the Securities and Exchange Commission, or the SEC, with the understanding that

our actual future results may be materially different from what we expect. These forward-looking statements are based on management’s

current expectations. These statements are neither promises nor guarantees, but involve known and unknown risks, uncertainties and other

important factors that may cause our actual results, performance or achievements to be materially different from any future results, performance

or achievements expressed or implied by the forward-looking statements. Factors that may cause actual results or events to differ materially

from current plans, intentions, and expectations include, among other things, those listed under Part I, 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.

iii

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:

iv

● We may be at risk of securities class action litigation.

v

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 18 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 millions 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.

1

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, one

in preclinical studies and one candidate and our ADC program in research optimization. One of the two drug candidates in clinical development,

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. All of these drug candidates and our ADC program 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. 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 preparing LP-300 for the launch of a targeted

phase II trial, in never smoking patients with NSCLC in combination with chemotherapy, under an existing investigational new drug application.

LP-100 is in a Phase II clinical trial in metastatic, castration-resistant, prostate cancer that was previously managed by Allarity Therapeutics.

As a result of the Asset Purchase Agreement we entered into with Allarity in July 2021, we obtained full authority to manage and guide

future clinical development and commercialization of LP-100.

Additionally,

we have one new drug candidate, LP-184, in 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 the drug, if approved. Subject to regulatory clearance to move forward under a future IND application, we are planning

a Phase I clinical trial for LP-184 to begin later in 2022. Our drug candidate, LP-284, the stereoisomer (enantiomer) of LP-184, has shown

promising in-vitro anticancer activity in a range of hematological cancers, which are distinct from the indications targeted

by LP-184. Our ADC program commenced in early 2021 is aimed at identifying targeted or therapeutic antibodies to conjugate with selected

compounds.

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.

2

As part of our overall growth

strategy, we plan to grow our pipeline by identifying new drug candidates and pursuing potential indications for LP-184, LP-300, 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 2022, we own or control over 80 active patents and patent applications across 14 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 from AF Chemicals, and BioNumerik

that are 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. 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) 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.

We use our RADR®

platform to identify potential biomarkers for patient response to a drug candidate and we further intend to validate the selected

drug candidate and potential associated biomarkers by conducting small, focused early phase clinical trials. We intend to create various

exit opportunities for each drug candidate that progresses successfully. For each drug candidate that progresses, along with its newly

identified biomarker diagnostic potential for drug response, we intend to partner, out-license, or internally develop the drug.

3

Our current pipeline of development

programs involves four small molecule drug candidates: LP-100, LP-300, 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

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.

4

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 a decade 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.I. by the biopharma and drug discovery community to approximately $4 billion by 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 AI 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 AI 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 on

a routine basis 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. 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).

5

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 and modules of our platform are depicted in the image below.

6

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.

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.

7

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 18 billion oncology-specific clinical and preclinical data points,

more than 154 drug-cancer interactions, and over 100,000 patient records from 16 databases, one of which is our internal database. Our

long-term objective is to collect and analyze over 25 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%.

8

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. Each component of the platform’s

architecture is unit tested and then integration tested to ensure functions and programs are working as designed. In order to track modifications

in the software, a version control system is in place. Detailed documentation has been created to record the design and architecture of

our platform.

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.

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, 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.

9

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:

● Continue to select and launch additional clinical development program.

10

LP-300

General Overview

We are currently advancing

LP-300 towards launch of a Phase II clinical trial (the “HARMONICTM 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 non-smoking (or

never-smoker) NSCLC patients with histologically defined adenocarcinoma. Since obtaining LP-300 rights from BioNumerik, we have not yet

conducted further clinical testing of LP-300. 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. 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 patients 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. 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

11

Upcoming LP-300

Phase II Clinical Trial

We

are preparing to launch a Phase II clinical trial (the “HARMONICTM 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.

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 will consist 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:

12

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.

13

Rationale Behind LP-300 Rescue and Repositioning

Efforts

Based on the results from

the prior Phase III NSCL adenocarcinoma trial, we are preparing to launch a new 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. 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.

Disease Background and Opportunity

Lung cancer remains one of

the most common and deadly cancers worldwide. Lung cancer accounts for 13% of all new cancer diagnoses, but 25% 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 2022 are:

14

The most common type of lung

cancer is called non-small cell lung cancer (“NSCLC”), which represents about 84% 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 the SEER Cancer

Statistics Review (November 2018) published by the National Cancer Institute 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 8 to 10 months; approximately one-third of patients will survive for 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. Mutant BRAF and ERBB2 are also investigational targets. 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). 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. There are

no current KRAS-mutated NSCLC-targeted therapies but there are targeted therapies for the indication by targeting downstream pathways

- for example mTOR inhibition. 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.

15

In the US in 2021, there were an estimated 15,750

diagnosed cases of NSCLC in female non-smokers, accounting for approximately 6.7% of all lung cancer cases. Globally in 2021, there were

an estimated 147,027 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:

● Younger;

● Asian;

● 2-3 times more likely to be non-smokers;

● more likely to develop adenocarcinoma and;

● more likely to have metastatic disease.

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 2018 estimates published by Global Cancer Observatory

and 2021 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:

Lung cancer Global US

Total Potential Patient Segment in New Lung Cancer 5% - 8 % 5% - 8 %

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.

16

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.

We believe it is important

to pursue the development of novel therapies and combinations thereof that can substantially improve patient survival and quality of life

by potentiating the antitumor activity of chemotherapy treatment while protecting against chemotherapy-induced toxicity.

Market Opportunity

Most never-smoker patients

with lung cancer are women, and adenocarcinoma is the most common type. Non-smoker patients with non-small-cell lung cancer (“NSCLC”)

generally have a better response to inhibitors of epidermal-growth-factor receptor (EGFR) tyrosine kinase, including without limitation

gefitinib and erlotinib, than do those with a history of tobacco smoking. Studies have identified differences in chromosomal aberrations,

genetic polymorphisms, gene mutations, and methylation status between lung cancer in non-smokers and tobacco-associated lung cancer. These

clinical and biological differences suggest that the two cancers have overlapping but unique pathways of carcinogenesis. The EGFR mutation

is one of the most important genetic change in lung cancer in people who have never smoked because it is more common in lung cancer in

never-smokers than in tobacco associated lung cancer and is associated with greater therapeutic benefit from inhibitors of EGFR. Other

alterations associated with never-smokers include mutations, fusions or amplifications in ALK, ROS1, RET and MET genes. Based upon published

articles in CA: Cancer Journal for Clinicians and Nature Review Cancer, incidence in never-smokers is 10% to 15% of all lung cancers and

globally, NSCLC in never-smokers comprises 15% to 20% of cases in men and greater than 50% in women. In Asia, never-smokers with NSCLC

are 60% to 80% women and 20% to 40% men.

We are focused on advancing

LP-300 as a potential combination therapy for non- or never smoking NSCLC patients with adenocarcinoma by leveraging our A.I. platform

to help uncover the genomic and biomarker networks that are associated with response in the never-smoker and non-smoker groups.

Additionally, through our early, preclinical work to define a gene signature that correlates with heightened sensitivity to LP-300, we

believe there is potential to further expand the indication to include all NSCLC patients that have this identified genetic profile in

their cancer. 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. If successful, LP-300 could provide improved patient benefit in terms of improved

survival, and secondarily through the concurrent prevention and mitigation of common and serious chemotherapy-induced toxicities.

17

Prior Completed Trials of LP-300

Phase I. LP-300 has

been evaluated in five Phase I studies (DMS10001, BioNumerik, 09/1997 through 04/2004; DMS10002, BioNumerik, 12/1997 through 08/2001;

DMS12209, ASKA Pharmaceutical, 04/2000 through 12/2001; DMS10011, BioNumerik, 02/2006 through 07/2006; and DMS12307, Baxter, 07/2002 through

07/2005) to determine the maximum tolerated dose (“MTD”), and to evaluate the safety, tolerability, pharmacokinetics, and

potential efficacy of LP-300 (alone or in combination with cisplatin, cisplatin/paclitaxel, or carboplatin/paclitaxel). An MTD for LP-300

was not reached in any of the Phase I studies at dose levels of up to 41 g/m2.

Phase II. In

a U.S. multi-center, randomized, open-label trial (n=160 patients) with advanced (Stage IIIB and IV) NSCLC treated with LP-300 or no LP-300

(DMS22210/CALGB 30303, Cancer and Leukemia Group B, 08/2004 through 03/2007), although the overall population did not meet the pre-specified

primary endpoint, an analysis of a subgroup of patients with adenocarcinoma revealed that the difference in the median overall survival

period between the 2 treatment groups was statistically significant (LP-300 = 15.6 months, no LP-300 = 8.9 months; Log-rank p=0.0326),

and the median overall survival for patients who received LP-300 was 6.7 months longer than that of those who did not receive LP-300.

Phase III. LP-300 has

been evaluated in five Phase III studies: two in patients with metastatic breast cancer, with a primary endpoint examining the ability

to reduce platinum/taxane induced peripheral neuropathy, and three in patients with NSCLC or advanced primary lung adenocarcinoma. (DMS32205R,

ASKA Pharmaceutical, 08/2005 through 02/2008; DMS30203R, BioNumerik, 09/2001 through 10/2006; DMS30204R, ASKA Pharmaceutical, 04/2003

through 03/2006; DMS32206R, Baxter, 10/2002 through 04/2006; and DMS32212R, BioNumerik, 04/2010 through 06/2013) Although the overall

population did not meet the pre-specified primary endpoints in any of the trials, analysis of subgroups of patients in one multi-country

lung adenocarcinoma trial and one Japanese NSCLC trial revealed differences in the median overall survival between the two treatment arms

(with or without LP-300 treatment). The results from the two key lung cancer trials obtained from retrospective analyses are described

below:

18

The LP-300 arm of the multi-country

Phase III NSCLC adenocarcinoma trial also demonstrated safety profile advantages in terms of the potential to protect against chemotherapy-induced

kidney toxicity and chemotherapy-induced anemia. These observations complemented earlier clinical observations regarding LP-300’s

potential to protect against neuropathy and other chemotherapy-induced toxicities. Results from these trials indicate that treatment with

LP-300 may, in further clinical testing, lead to improved survival in female and non- or never smoking patients with primary adenocarcinoma

of the lung receiving cisplatin/paclitaxel combination chemotherapy.

Phase II and III LP-300 Adverse Events Summary

The following summarizes adverse

events reported from a total of 1,712 patients enrolled in five randomized multi-center phase II and phase III studies with chemotherapy,

with or without LP-300. A total of 1,712 patients were enrolled in these studies, of which 856 patients received LP-300 with chemotherapy.

19

Clinical Evidence of Toxicity Protection by

LP-300

The data from randomized multicenter

studies of LP-300 and chemotherapy demonstrates objective evidence of several instances where treatment with LP-300 appears to provide

potential benefit in terms of preventing and mitigating chemotherapy-induced toxicities, particularly in studies of LP-300 and chemotherapy

in patients with advanced NSCLC. These data support that LP-300 has the potential to protect against chemotherapy-induced toxicities,

including gastrointestinal, renal, electrolyte disturbances, and anemia; and there is data supporting the potential for LP-300 to protect

against severe forms of these toxicities. In addition, treatment with LP-300 may protect against severe platinum-induced hearing loss

and dehydration.