Filed pursuant to Rule 433 of the Securities Act of 1933 Issuer Free Writing Prospectus dated July 15, 2020 Relating to the Preliminary Prospectus dated June 26, 2020 Registration Statement File No. 333-238153 Artificial Intelligence Neural Network for Target Prediction

Disclaimer This presentation is provided by Kiromic BioPharma, Inc. (the “Company”) for the express purpose of giving prospective investors, bankers, employees, consultants, and corporate affiliations information regarding the Company to assist the recipi-ent in evaluating a potential formal associa-tion with the Company. While the informa-tion contained herein or in any other mate-rials that may be provided by the Company is believed to be true, accurate and reason-able, the Company makes no such represen-tation or warranty, express or implied, as to the veracity, accuracy, reasonableness or completeness of such information. The Company expressly disclaims any and all liability which may be based on such infor-mation, any errors therein or omissions therefrom. This presentation does not imply an offering of Securities. This presentation may contain forward-looking statements within the meaning of applicable securities regulations. All statements other than state-ments of historical facts are forward-looking statements. In some cases, forward-looking statements may be identified by the use of words such as "anticipate," “believe,” "plan," “estimate,” "expect," "intend," "may," "will," "would," "could," "should," “might,”“poten-tial,” or "continue" and variations or similar expressions. Readers should not unduly rely on these forward-looking statements, which are not a guarantee of future performance. There can be no assurance that for-ward-looking statements will prove to be accurate, as all such forward-looking state-ments involve known and unknown risks, uncertainties and other factors which may cause actual results or future events to differ materially from the forward-looking state-ments. Such risks include, but may not be limited to: general economic and business conditions; technology changes; competi-tion; changes in strategy or development plans; governmental regulations and the ability or failure to comply with governmen-tal regulations; the timing of anticipated results; and other factors referenced in the Company’s business materials and prospec-tuses. 2

Free Writing Prospectus Kiromic Bioharma, Inc. (“we“ or “us”) has filed a registration state-ment (including a preliminary prospectus) (the “Registration State-ment”) with the Securities and Exchange Commission (the “SEC”) on Form S-1/A (SEC File No. 333-238153) for the offering to which this presentation relates. You should read the prospectus in the Registration Statement and other documents that we have filed with the SEC for more com-plete information about us. You may access these documents for free by visiting EDGAR on the SEC web site at www.sec.gov or by contacting Thinkequity, a Division of Fordham Financial Mgmt., Inc., 17 State Street, 22nd Floor, New York, NY 10004, by telephone at (877) 436-3673 or by email at prospectus@think-equity.com. Such registration statement has not yet become effective. Shares of our common stock may not be sold, nor may offers to buy be accepted, prior to the time the registration statement becomes effective. Before you invest, you should read the preliminary pro-spectus and other documents we file with the SEC for more com-plete information about our company and this offering. 4

Offering Summary Proposed Aggregate Offering $25,000,000 Price Range $12.00 -14.00 per share Proposed Symbol KRBP (NASDAQ Capital Markets) Shares Offered 1,923,100 Shares Pre-IPO Common Shares 6,083,000 Shares as converted (07/06/2020) 8,006,100 Shares Advancing clinical development of PD-1, Iso-Mesothelin, our lead CAR-iNKT for the treatment of solid tumors, R&D working capital, general corporate Post-IPO Common Shares Advancing clinical development of PD-1, Iso-Meso, Our lead CAR - iNKT candidates for treatment of Solid Tumors Use of Proceeds Sole Book Runner ThinkEquity, a division of Fordham Financial Management, Inc. 3

Non-Viral Genome edit and delivery Our single-cut gene edits carry a lower mutagenesis risk vs. classic double-cut gene edits. Our CAR receptors will also have higher safety with an on-demand cut-off switch vs. classic CAR therapies with no off-switch. Artificial Intelligence Neural Network for Target Selection dCAS9 OH gRNA Integrase We are connecting the dots in cancer research by using AI and machine learning to connect silos of informations and arrive at cancer targets which will be more effective vs. classic development, saving man-years and billions in development dollars. ABBIE Kiromic Gamma Delta T-cell Immune Cell Type Our CAR Therapy will be using off-the-shelf Gamma-Delta T-cells and will have a higher yield and significantly lower yield variability vs. classic CAR-T therapies. at a Glance Micro Tumor Environment Our CAR Therapies will be able to access the micro tumor environment due to our PD-1 check-point inhibitor vs. classic CAR-T therapies. Classic CAR-T are limited to hematologic indications. Solid Tumor 5

Artificial Intelligence Neural Network for Target Selection Diamond is a computational platform and a neural network that can identify new cancer immunological targets for T cells and B cells. Diamond is an artificial intelligence and machine learning approach that can identify novel surface tumor targets. It uses public and proprietary samples and can expand into the tumor target space. ADVANCING CAR through A.I. 6

Big Data Science meets Target Identification dramatically compressing Big Data Science Manual Target Identification Man-Years and Billions of Drug Development to develop a live drug Dollars 7

Artificial Intelligence Engine’s Compression for live drug development of Time & Costs Classic Chemistry Phase 1 Phase 2 Phase 3 Pre-clinical Regulatory Classic Small Molecules + Classic Targets Small Molecule A.I. Target Prediction CAR-Therapy 8 Target Identification & Validation

Management CEO Director CFO COO Director Maurizio Chiriva-Internati, PhD Mr. Chiriva-Internati is an associate professor at MD Anderson Cancer Center. He has spent the past 28 years studying cancer targets and is the founder of Kiromic Artificial Intelligence Neural Network. He has published +160 articles (+peer reviews) on cancer targeting and on the use of AI to expedite the search for these targets. He holds PhD in immunology (U of Nottingham), PhD in morphological science (Milan), and a Certificate in Artificial Intelligence - M.I.T. Tony Tontat Mr. Tontat brings to Kiromic over 2 decades of business experience from public (NASDAQ: SRNE, NK) and privately held biotechs. He had been healthcare analysts at specialist healthcare investment funds in New York, and Connecticut. He was also an investment banker at HSBC Securities in their New York, London, and Paris offices. Bachelor of Arts in Economics - Harvard University. CSIO Director CMO Gianluca Rotino Chief Strategy, Innovation Officer Mr. Rotino held CEO and Chairman roles in several Italian companies specializing in high-tech, and corporate consulting. He also worked at law firms in Milan where he specialized in M&A, intellectual property prosecution and corporate law. He holds a business development degree and bachelor of science (Pharma focus) - EBD Academy in London, and completed the drug discovery, develop. and commercialization - U.C. San Diego. Scott Dalhbeck, MD, PharmD Dr. Dalhbeck was a radiation oncologist and was an adjunct professor of internal medicine, pathology, and urology at Texas Tech. He has also patented, manufactured, and commercialized IP and has more than a decade of experience in medical and oncology commerce. He holds an MD - Texas Health Science Center, and a PharmD - U of Nebraska. His residency was at Kaiser Permanente of Los Angeles. 9

Processes non-exhaustive list of functions being applied by A.I. Engine Prioritizing T and B Cell Targets Diamond generates a prioritized list of cancer immunological targets for T cells and B cells. Identify Highly Expressed Genes Diamond’s cognitive and deep learning capabilities extract information from our extensive digital library consisting of clinical studies, genomic and proteomic datasets. Perform Meta Analysis Diamond performs meta-analysis and convolution studies while standardizing and normalizing data across multiple and variable experimental platforms, then allows for the visualization of consistent and accurate results in a user-friendly fashion. Predict Isoform Targets Cancer cells will down regulate or shed targets in order to avoid detection and destruction by T cells (the immune system). These targets can be used to create therapies such as antibody therapies, T cell therapies, T cell receptor therapies, CAR T cell therapies and vaccine therapies. These variations are known as isoforms. Diamond harmonizes all the raw data and creates datasets which allows us to screen for cancer targets. CancerSplice also shows a box plot by tissue of expression of the isoform in normal cancer genome atlas tissues and a box plot of the matching isoform in genotype-tissue expression program normal data. Diamond will identify and prioritize lists of genes (biomarkers, wild type, mutant, isoform, neoepitope, etc.) that are highly and specifically expressed in the disease of interest while providing its distribution and methylation status across the entire patient population. The sequence of amino acids that are specific for the selected cancer isoforms are then directly fed to Diamond’s artificial neural capsule network for peptide design and prioritization. It also maps out the exact portion of the gene that will elicit an immune response. 10

Step 1 Databases Big Cancer immune peptides database Public Databases The Cancer Genome Atlas Private A.I. Engine Servers 11

Step 2 Artificial Intelligence Engine +histo-aminochemistry filters, +machine learning Diamond Proprietary Diamond Proprietary iCloud Web Client Accessible r-Epitope Epitope Selection Cancer Splice Iso-form Selection 3D Visualization Servers Servers Iso-forms Target Selection & Prediction 12

Step 3 Prediction A.I. Prediction Artificial Intelligence Selected Target Heatmap of T-cell, B-cell epitopes High expression in cancer cells Low expression in normal cells High affinity to TCR Quantity surface antigen expression signature 13

Step 4 Target Validation We rigorously validate all targets from our A.I. Prediction Engine Internal validations and then external validation Wet Lab Validation Algorithm Validation Yale Baylor University University of Rome 14

CancerSplice TM A key A.I. Engine Target isoforms are protein variants of the same targets that occur during the normal processing of immature gene transcripts to the mature form. Target isoforms include variations in their primary amino acid sequence that can change both the If they are the predominate form on the cell surface, these isoforms can make it impossible for T cells to outright bind the targets on cancer cells. No binding or insufficient binding to the isoform results in no killing of cancer cells. Our CancerSplice accurately predicts the most appropriate isoforms for T cells to bind and destroy cancer cells. final folded form of the target plus be recognized by modified T cells (autologous/allogeneic) and other their ability to cells, such as NK or invariant NKT cells (often used in the allogeneic setting). 15

Targets Which We Have Identified How our identified targets are developed into therapies for live drugs to treat cancer AIDT-1 Target Iso Mesothelin Target Iso Mesothelin Target Hematology MPM EOC (Malignant Pleural Mesothelioma) Solid Tumor - Lung (Epithelial Ovarian Cancer) Solid Tumor - Ovarian 16

Indications: By the Numbers Ovarian Cancer MPM - Lung Cancer Hematolo3gical Cancers 300,000 Worldwide number of patients American Cancer Society 43,000 Worldwide number of patients American Cancer Society 200,000 Worldwide number of patients American Cancer Society 21,750 2018 new cases in the USA American Cancer Society 2018 3,000 Annually in the USA American Cancer Society 2018 30,000 Annual Diagnosis in the USA American Cancer Society 2018 $1.2 BLN in 2018 Grand View Research (July 2019) $300 M by 2025 Persistent Market Research, Jul 2017 $4.6 BLN BIS Research, Nov 2019 by 2025 17

Our Pipeline PD-1 (Solid Tumors) PD-1 Isoform Mesothelin Check-Point Inhibition This is our lead target candidate which came out of our Artificial Intelligence Prediction Engine. Isoform targets are highly expressed on cancer cells while very lighly expressed on normal (healthy) cells. A protein found on T cells (a type of immune cell) that helps keep the body’s immune responses in check. When PD-1 is bound to another protein called PD-L1 (tumor) , it helps keep T cells from killing other cells, including cancer cells. Some anticancer drugs, called immune checkpoint inhibitors, are used to block PD-1. When this protein is blocked, the “brakes” on the immune system are released and the ability of T cells to kill cancer cells is increased. 18 In vitro validation Pre clinical IND Phase 1 Phase 2 Phase 3 Alexis (γδ-T cells) Allogenic / Iso-Mesothelin EOC (Solid, Ovarian) Alexis (γδ-T cells) Allogenic / Iso-Mesothelin MPM /Pleural mets (Solid, Pleural) Alexis (γδ-T cells) Allogenic / AIDT-1 (Hematologic Indications) check point inhibitor

Clinical 2021 Programs 2020 2022 2023 4Q 4Q 4Q Proof of Concept Phase 1 PD-1 + Iso-Meso Phase 1/2 Ovarian PD-1 IND PD-1 Target Dosing Iso-mesothelin Iso-Meso Efficacy, Observation Cell Type Gamma-Delta Cells sub-type of immune cells Solid Tumors: PD-1’s Role Classic CAR-T therapies are currently not being used in solid tumors. Solid tumors have PD-1s in their micro tumor environment. PD-1 put the “brakes” on T and NK Cells’ killing of antigens (tumor cancer cells). PD-1 + Iso-Meso Phase 2 MPM Lung IND Phase 1 Dosing Safety Arm PD-1 + Meso Efficacy, Observation PD-1 + Iso-Meso Gamma-Delta Cells PD-1 + AIDT1 Phase 2 Hematology IND By inhibiting PD-1, our CAR Therapies will be able to access the micro tumor environment and kill solid tumor cancer cells which have eluded killing by classic CAR-T therapies. Phase 1 Dosing Safety Arm PD-1 + AIDT1 Efficacy, Observation Hematology Gamma-Delta Cells 19

ADVANCING CAR through Our Therapeutic Products Allogenic CAR Immuno CAR-GD-T Therapy in solid tumors A.I. 20 CAR = chimeric antigen receptors

Allogenic Engineered Immune Therapy Step 01: Fractionation Healthy Donor Screening shows donor has healthy Gamma-Delta T cells Whole Blood Fractionation Gamma-Delta T cells extracted 21

Allogenic Engineered Immune Therapy Step 02: Genome Edit dCAS9 OH gRNA Integrase ABBIE Genome Edit Gamma-Delta T cells Proprietary Gene Edit Mechanism 22

ABBIE: Non-Viral Genome Editing Mechanism The ABBIE protein accompanies the CAR-containing genome template as it passes through the cell membrane into the nucleus and guides the template-flanking sequences (the “glue”) safely into the target genome. Due to this targeting ability, ABBIE can also be used to remove unwanted, inhibitory genes. CAR expression on the Gamma-Delta T cells allows them to detect and destroy the antigen-expressing targeted cells. The OFF switch permits fast shutdown in the event of an unexpected toxicity. Additional Anti-tumor factors can help neutralize the toxic tumor microenvironment. ABBIE is a novel gene-editing system for inserting therapeutic genes safely into the genome of a host cell. ABBIE technology comprises two main components, (i) a genome template (extracted from the ALEXIS plasmid), containing the therapeutic genes needed to retrain tumor-killing cells, and (ii) the gene-editing machinery required to safely insert this template into the genome the therapeutic cells. of 23

ABBIE Gene Linear Non-viral Template Editing Technology dCAS9 CAR gRNA Promoter LTR Off-switch Anti-tumor LTR Integrase dCAS9 Figure 1. Our ABBIE gene-editing technology begins with the transgene template plasmid. Plasmid DNA is cut with restriction enzyme, ScaI, liberating the transgene template along with the retroviral-derived long-terminal repeats (LTRs), which is Figure 3. The guide RNA (gRNA) tethers ABBIE-bound template to the target site via DCas9, and Integrase helps to attach the exposed 3’OH groups to the target site on both strands without causing a dsDNA break. dCAS9 purified away from the plasmid DNA and ScaI protein. OH gRNA Integrase LTR Integrase ABBIE LTR OH dCAS9 gRNA Figure 2. The ABBIE integrase, derived from HIV, is added, which binds to the LTRs and exposes a reactive 3’-OH group on each end. Figure 4. Following stable integration of the template into the target DNA locus, a short DNA duplication is present on each end. 24

ABBIE Integrase binds to the LTRs and exposes a reactive dCAS9 3’-OH group on each end. OH gRNA Integrase Integrase ABBIE OH dCAS9 gRNA 25

Allogenic Engineered Immune Therapy Step 03: Gamma-Delta T Cells expanded invitro Chimeric Antigen Receptor CAR CAR CAR CAR Receptor CAR Expansion CAR CAR Engineered Gamma-Delta T Cells expanded invitro Gamma-Delta T Cells start expressing CAR Receptors 26

How We Know: GD-T cell Expansion Works γδ T cell phenotyping zoledronic acid +IL-2 Day 0 γδ T PBMC Day 7 γδ T Before Enrichment γδ T cell product second expansion γδ T cell initial expansion γδ T cell negative selection & transduction γδ T cell first expansion PBMC Isolation DAY 0 DAY 7 DAY 14 Day 7 γδ T After Enrichment A large fold of expansion of highly pure γδ T cells during in vitro stimulation, culture, isolation and expansion process. γδ T Purity γδ T Expansion Day 14 γδ T Cells 15,000 100% 80% FSC-H CD3 TCRVγ9 10,000 60% 40% 5,000 CONCLUSION Our method of γδ T expansion yield highest 12,000-fold expansion of γδ T cells, which is over 95%purity for posi-tive for CD3, Vγ9, and Vδ2. This has potential to produce enough number γδ T for clinical use. 20% Day 0 7 7 14 Day 0 7 14 Pre-selection Post-selection The percentage of CD3+γ9+δ2+ T cells over 14-day culture. The expansion fold of CD3+γ9+δ 2+ T cells with our method. 27 CD3+γ9δ2+T % Folds of Expansion SSC-H

Allogenic Engineered Immune Therapy Patient Step 04: GD-T Cells infused into CAR CAR Gamma-Delta T cells CAR Off-The-Shelf CAR CAR CAR Engineered Immune Cell Therapy Gamma-Delta T cells Gamma-Delta T cells Patient receives engineered Immune Cell Therapy 28

Up-Armoring Accessory proteins can “up-armor” cellular therapies Strategic choice of proteins to improve anti-immune responses cellular function and neutralize * Activated signaling molecules chosen to enhance cell persistence by stimulating cytokine pathways Targeting the immunosuppressive “reactive” stroma can enable tumor targeting by therapeutic cells while increasing anti-tumor efficacy Linear Non-viral Template CAR Promoter * LTR Off-switch Anti-tumor LTR 29

dCAS9 Switches OHgRNA Integrase ABBIE ON OFF ON OFF ON OFF ACTIVATION Switch ATTENUATION Switch SAFETY Switch A rapidly deployed activation switch can provide a survival and proliferation signal to the therapeutic cells to enhance their efficacy and persistence in vivo. A rapidly deployed attenuation switch can intercept activation signals transiently to minimize toxicity following successful anti-tumor interactions. Choice of two non-mutually exclusive Attenuation Switch approaches: A rapidly deployed, protein-based safety switch can eliminate therapeutic cells in case of acute toxicity.The safety switch is designed to eliminate either: (a) (b) essentially all active therapeutic cells. only the most active cells, preserving a cohort of backup therapeutic cells for long-term control of residual relapsing tumor cells. (a)a protein-based switch that rapidly triggers attenuation of target cells in a dose-dependent fashion. The Safety Switch will be co-expressed along with the bioactive chimeric activation receptor (CAR), the Activation Switch, and the Attenuation Switch. (b) a small molecule-based approach to rapidly and reversibly attenuate cell signaling. 30

Use of Proceeds IP costs 1.9% G&A 10.9% Clinical 23.4% Biochemistry $25M Raise R&D CapEx 3.7% R&D 60.1% A.I. Engine 31

Comparables A.I. Targets + Small Molecules CAR-T and CAR-NK $6.02 BLN IPO Feb 2020 $3.06 BLN Public $1.18 BLN IPO Jan 2020 $4.36 BLN Public $1.08 BLN IPO Mar 2018 $11.9 BLN Acquired $79 M IPO Jun 2020 $9.0 BLN Acquired 32 Market data as of 07/13/2020, Yahoo Finance intraday

Cap Table Valuation Valuation drivers are blended values from the following: Capitalization (07/13/2020) Common Stock * 6,083,000 Artificial Intelligence Predicted Targets (vs. classic targets) Option (WAEP $11.84) RSU (WAVG GDFV $19.00) 617,999 709,334 CAR Therapies (vs. classic small molecule) Fully Diluted Common 7,410,332 CAR Allogenic (Gamma-Delta T cells) (vs. classic CAR-T autologous) *Includes conversion of 21,822,301 Shares of Series A-1 Preferred Stock into 624,594 shares of common stock; 16,391,397 shares of Series B Preferred Stock into 469,136 shares of common stock Solid Tumor (PD-1 checkpoint inhibition) (vs. classic CAR-T liquid cancers) Company’s info as Tuesday, 07/13/2020 33

dCAS9 Non-Viral Genome edit and delivery Our single-cut gene edits carry a lower mutagenesis risk vs. classic double-cut gene edits. Our CAR receptors will also have higher safety with an on-demand cut-off switch vs. classic CAR therapies with no off-switch. OH gRNA Integrase ABBIE Artificial Intelligence Neural Network for Target Selection We are connecting the dots in cancer research by using AI and machine learning to connect silos of informations and arrive at cancer targets which will be more effective vs. classic development, saving man-years and billions in development dollars. Value Drivers Our CAR Therapy will be using off-the-shelf Gamma-Delta T-cells and will have a higher yield and significantly lower yield variability vs. classic CAR-T therapies. Our CAR Therapies will be able to access the micro tumor environment due to our PD-1 check-point inhibitor vs. classic CAR-T therapies. Classic CAR-T are limited to hematologic indications. Solid Tumor Micro Tumor Environment Gamma Delta T-cell Immune Cell Type 34

THANK YOU 7707 Fannin Street, Suite 140 Houston, Texas 77054 +1 (806) 368 - 6731 ttontat@kiromic.com 35

Intellectual Property dCAS9 OH gRNA Integrase Artificial Intelligence Neural Network for Target Selection ABBIE Platform for Identification of Tumor-Associated Cancer/Testis Antigens (15/731,143 Utility Application) – PD1-Specific Chimeric Antigen Receptor as an Immunotherapy (PCT/US2018/052799 US Application and PCT Application) – Claims: Composition of matter claims for nucleic acid constructs; organisms comprising nucleic acid construct; fusion proteins; and nucleic acid vectors. The claims in this patent family also contain methods of inserting a DNA sequence into genomic DNA and inhibiting gene expression. Claims: Composition of matter, use and process for a method of identifying cancer/testes antigens (CTAs) useful as cancer treatment targets, the method comprising: identifying human sperm proteins to which patients diagnosed with solid or hematological malignancies have established a humoral immune response Claims: Composition of matter claims for a chimeric antigen receptor (CAR) polypeptide; a vector comprising the CAR polypeptide; and a T lymphocyte genetically modified to express the CAR polypeptide. The claims in the patent application also contain a method of treating cancer using the T lymphocyte genetically modified to express the CAR polypeptide. Method for the identification and use of hot-spot mutations and tumor-associated splice isoforms in cancer immunotherapy (62/921,127 Provisional Application) – No claims filed NK-engineered cell lines for the treatment of cancer (62/921,537 Provisional Application) – No claims filed CAS 9 Retroviral Integrase and CAS 9 Recombinase Systems for Targeted Incorporation of a DNA Sequence into a Genome of a Cell or Organism (PCT/US2016/025426) – Anti-Human/Mouse Sperm Protein 17 (SP17) Antibody and Derivatives Thereof (15/530,964 Utility Application) - Claims: Composition of matter, use, and method for a novel monoclonal antibody, designated as GD6, and various derivatives thereof, which target an epitope of human and murine Sperm Protein 17 (SP17) which possesses broad expression on cells derived from numerous solid malignancies 36

Directors Maurizio Chiriva-Internati, PhD Tony Tontat Gianluca Rotino Independent Jerry Schneider, JD, MBA He currently serves on the board of directors and audit committee for Cognex, a provider of vision systems, software, sensors, and industrial barcode readers used in manufacturing automa-tion since 2016. Cognex (CGNX) is publicly traded on the Nasdaq stock exchange. He serves on other for-profit and non-profit boards. Mr. Schneider received his Juris Doctor from Loyola Law School, and a B.S. in Accounting from the University of California at Berkeley. He has experience of being a ‘‘financial expert’’ appointed by the U.C. Regents which oversee the University of California’s budget of over $30M. Independent Michael Nagel Mr. Nagel has served as a member of our board of directors since June 2020. He has over 30 years of sales and marketing experience in the medical device industry. Since 2012, Mr. Nagel has served as the President and CEO of Vomaris Innovations, Inc, which specializes in wireless microcurrent-generating technologies that are focused on regeneration, healing, and recovery. Previously, Mr. Nagel served as the Chief Commercial Officer of Neomend, a biomaterial company that developed ProGel, a PMA approved surgical sealant for lung surgery. From 1997 to 2005, Mr. Nagel also served as Co-Founder and Vice President of Worldwide Sales and Marketing at Vascular Solutions (VASC). Independent Americo Cicchetti, PhD Dr. Cicchetti has served as a member of our board of directors since March 2020. Dr. Cicchetti has served as a Professor of Management at Universit`a Cattolica del Sacro Cuore, Faculty of Economics, Rome since 2006. He is also currently the Director of the Graduate School of Health Economics and Management at Universit`a Cattolica del Sacro Cuore. In addition to his academic experience, Dr. Cicchetti was a member of the Price and Reimbursement Committee of the Italian National Drug Agency from 2009-2015. He is a member of the European Network of Health Technology Assessment; Member of the Innovation Steering Group of the National HTA Program for Medical Devices (Ministry of Health, Italy); Member of the National Immunization Technical Advisory Group at the Ministry of Health, Italy since 2019; Member of the Health and Research Commission of the Rome Foundation since 2007; and a Member of the Board of Directors of the Health and Research Foundation since 2017. Independent Pietro Bersani, JD, CPA Mr. Bersani has served as a member of our board of directors since June 2020. Since April 2020, Mr. Bersani is a Partner with B2B CFO Partners, LLC, which provides strategic management advisory services to owners of privately held companies. During October 2016 and July 2018, he served as the President, and Chief Executive Officer at K.P. Diamond Eagle, Inc., a consulting firm specialized in development of innovative commercial and private aviation business models. He also held the same positions at K.P. Diamond Eagle, Inc. between November 2019 and March 2020. He later served as a Senior Director within Alvarez & Marsal’s Private Equity Performance Improvement Practice, LLP between August 2018 and October 2019. Prior to those professional experiences, Mr. Bersani served as the Chief Financial Officer of Fuel Systems Solutions, Inc. between April 2011 and October 2016. In addition to Mr. Nagel’s executive experience, he also serves as a director for Franklin Mountain Medical, LLC an early stage company in the structural heart market. Mr. Nagel holds both a B.A. in Business and a M.B.A. from the University of St. Thomas. Mr. Bersani is a Certified Public Accountant and is also a Certified Public Auditor and a Chartered Certified Accountant in Italy where he developed a significant knowledge of US GAAP and IFRS. Mr. Bersani earned a BA and MA in Business Economics from L. Bocconi University, Italy. Mr. Bersani was designated by certain holders of our Series B Preferred Stock. Except for the foregoing, there is no arrangement or understanding between any director or executive officer and any other person pursuant to which he was or is to be selected as a director. Furthermore, Dr. Cicchetti is the Chief Executive Officer and Director for Molipharma, whose core business is the research and development of new drugs and diagnostics aimed at predicting, detecting and treating female oncological diseases. He also serves as an independent board member for Foundation Health and Research, and Leonida SICAF, a fixed capital investment company. He obtained his PhD in Management from University of Bologna, and his B.A. from University of Rome. 37

Artificial Intelligence Neural Network 162 Internal Publications Chiriva-Internati M, Cobos E, Cannon MJ. Editorial: Prospects and Chal-lenges for Immunotherapy of Ovarian Cancer-What Can We Learn from the Tumor Microenvironment? International Reviews of Immunology. 2011;30(2-3):67-70. Maurizio Chiriva-Internati, Leonardo Mirandola, Franco Marincola, Gianluca Rotino, Jose A. Figueroa, Fabio Grizzi, and Robert Bresalier The Quest for the Next-Generation of Tumor Targets: Discovery and Prioritization in the Genomics Era. Springer, Published 2019.( chapter for a book “immune-Oncology: Cellular and Translational Approaches” 2019 Cannon MJ, Goyne H, Stone PJB, Chiriva-Internati M. Dendritic cell vaccination against ovarian cancer - tipping the Treg/T(H)17 balance to therapeutic advantage? Expert Opinion on Biological Therapy. 2011;11(4):441-445. Maurizio Chiriva-Internati & Adrian Bot. New Era in Cancer Immunotherapy: Discovering Novel Targets and Reprogramming the Immune System. International Reviews of Immunology, 34:2, 101-103. 2015 Wachtel MS, Zhang Y, Xu T, Chiriva-Internati M, Frezza EE. Combined hepatocellular cholangiocarcinomas; analysis of a large database. Clin Med Pathol.1:43-7. 2008 Grizzi F, Gaetani P, Tancioni F, Di Ieva A, Bollati A, Baena R, Dioguardi N, and Chiriva-Internati M. From Discovery to the Clinical Application In Nervo System Neoplasia. In: Tumor Associated Antigens, 2004. Mirandola L, J Cannon M, Cobos E, Bernardini G, Jenkins MR, Kast WM, Chiriva-Internati M. Cancer testis antigens: novel biomarkers and targeta-ble proteins for ovarian cancer. Int Rev Immunol. Apr-Jun;30(2-3):127-37. doi: 10.3109/08830185.2011.572504. 2011 Figueroa AJ ,Pena C ,Mirandola L, Reidy A , Payne D, Hosiriluck N, Suvorava N, Rahman LR , Whitlow AR ,Verma R , Cobos E and Chiriva-Internati M. “Therapeutic Monoclonal Antibodies and Their Targets” by Wiley Production_Biosimilars of Monoclonal Antibodies: A Practical Guide to Manufacturing, Preclinical and Clinical Development, First Edition. Edited by Cheng Liu and K. John Morrow Jr. © 2017 John Wiley & Sons, Inc. Published 2017. Bumm K, Zheng M, Bailey C, Zhan F, Chiriva-Internati M, Eddlemon P, Terry J, Barlogie B, Shaughnessy JD Jr. CGO: utilizing and integrating gene expression microarray data in clinical research and data management. Bioinformatics. 2002 Feb;18(2):327-8.2002 Grizzi F, Russo C, Portinaro N, Hermonat PL, Chiriva-Internati M. Complexity and cancer. Gastro-enterology. 2004 Feb;126(2):630-1; author reply 631-2. PubMed PMID: 14765401.2004 38 Source: Pubmed.gov, search Chiriva-Internati (162 publications from 1997 to 2020)