Lantern Pharma : Company Overview

Nasdaq: LTRN

L e v e r a g i n g A . I . , m a c h i n e l e a r n i n g & g e n o m i c s t o t r a n s f o r m t h e c o s t , p a c e , a n d t i m e l i n e o f o n c o l o g y d r u g d i s c o v e r y a n d d e v e l o p m e n t

LTRN

Forward looking statements

This presentation contains forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. These forward-looking statements include, among other things, statements relating to: future events or our future financial performance; the potential advantages of our RADR® platform in identifying drug candidates and patient populations that are likely to respond to a drug candidate; our strategic plans to advance the development of our drug candidates and antibody drug conjugate (ADC) development program; estimates regarding the development timing for our drug candidates and ADC development program; our research and development efforts of our internal drug discovery programs and the utilization of our RADR® platform to streamline the drug development process; our intention to leverage artificial intelligence, machine learning and genomic data to streamline and transform the pace, risk and cost of oncology drug discovery and development and to identify patient populations that would likely respond to a drug candidate; estimates regarding potential markets and potential market sizes; sales estimates for our drug candidates and our plans to discover and develop drug candidates and to maximize their commercial potential by advancing such drug candidates ourselves or in collaboration with others. Any statements that are not statements of historical fact (including, without limitation, statements that use words such as "anticipate," "believe," "contemplate," "could," "estimate," "expect," "intend," "seek," "may," "might," "plan," "potential," "predict," "project," "target," "aim," "upcoming," "should," "will," "would," or the negative of these words or other similar expressions) should be considered forward-looking statements. There are a number of important factors that could cause our actual results to differ materially from those indicated by the forward-looking statements, such as (i) the impact of the COVID-19 pandemic, (ii) the risk that we may not be able to successfully initiate, conduct, or conclude clinical testing for or obtain marketing approval for our product candidates; (iii) the risk that no drug product based on our proprietary RADR A.I. platform has received FDA marketing approval or otherwise been incorporated into a commercial product,

and (iv) those other factors set forth in the Risk Factors section in our Annual Report on Form 10-K for the year ended December 31, 2020, filed with the Securities and Exchange Commission on March 10, 2021. You may access our Annual Report on Form 10-K for the year ended December 31, 2020 under the investor SEC filings tab of our website at www.lanternpharma.comor on the SEC's website at www.sec.gov. Given these risks and uncertainties, we can give no assurances that our forward-looking statements will prove to be accurate, or that any other results or events projected or contemplated by our forward-looking statements will in fact occur, and we caution investors not to place undue reliance on these statements. All forward-looking statements in this presentation represent our judgment as of the date hereof, and, except as otherwise required by law, we disclaim any obligation to update any forward-looking statements to conform the statement to actual results or changes in our expectations.

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The Golden Age of A.I. in medicine

10 Mega-Trends Setting The Stage for A.I. Led Transformation in Drug Development & Medicine

1. Large-scale,relevant and readily available data-sets
2. Methods, technologies and algorithms that are massively scalable
3. Computing, storage and transmission continue exponential advances
4. Rapid rise of global talent and collaboration networks
5. Tremendous increase in quality of biological data and methods
6. Rise of sequencing as a highly available, on-demand,low-cost service
7. Consumers willing to share personal data
8. Industries that have an increasing impetus to transform
9. New generation of investors demanding novel value creation
10. Executives and entrepreneurs rewarded for rapid change

Lantern is at the forefront of this model of A.I. driven transformation in the area of personalized oncology drug developmentto drive value for cancer patients and our investors

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Current oncology drug development is costly, risky, and inefficient … A perfect problem for artificial intelligence & machine learning to solve

Challenges in drug development …

3.3%	$2.8B
Avg. success rate of	R&D investment to bring
oncology drugs	new cancer to market
	2009-2018

17,000+	4-12X
Oncology trials	Success rate of oncology
conducted from	trials using biomarker
2001-2015

…are being met by data-driven, and A.I.-enabled

approaches & technology

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Thiebault Geoux, Ph.D. Chemistry - Elsevier 11/9/2015

There is a critical need to rescue drugs that failed or bring abandoned therapeutic assets to market in order to create ROI for biopharma

ROI Among The Top 15 Pharma -

Continuous Decade of Decrease

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10.1

%)	10
(IRR,
					7.6
Return						7.3
8													5.5
of
														4.8
Rate	6																		4.2	4.2
																						3.7
Internal	4																						1.9					1.7
Absolute
2																																		0.7
	0					R&D ROI
	2 010	2 011	2 012	2 013	2 014	2 015	2 016	2 017	2 018	2 019	2 020
	-2

Source: Deloitte research, 2020

Two Central problems

in Oncology drug development

1. "... low efficacies of cancer drugs might be attributed to the heterogeneity of the tested patient population, which essentially dilutes the strong therapeutic effect that a drug might have on a specific patient

subgroup."

2. "The ever-increasing catalog of genetic changes involved in cancer development is fueling a new generation of targeted drugs that are designed to address specific weaknesses in tumor cells. But these drugs will only work in a subset of patients - creating a demand for genetic stratification.

Allison Halliday, Ph.D.

Cancer Research, 01/31/2020

Cancer Biomarkers: Powering Precision Medicine

Solves these two central

problems in oncology drug

development with

unprecedented speed

and cost

This allows us to increase the potential for success and improve trial design

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