2023 Q1 Form 10-Q Financial Statement

#000142072023000009 Filed on February 14, 2023

View on sec.gov

Income Statement

Concept 2023 Q1 2022 Q4 2021 Q4
Revenue $0.00 $0.00 $168.0K
YoY Change -100.0% -100.0% -76.17%
Cost Of Revenue $113.0K
YoY Change -46.45%
Gross Profit $55.00K
YoY Change -88.87%
Gross Profit Margin 32.74%
Selling, General & Admin $3.525M $7.229M $8.362M
YoY Change -58.66% -13.55% 44.02%
% of Gross Profit 15203.64%
Research & Development $2.644M $2.779M $3.331M
YoY Change -52.37% -16.57% 40.31%
% of Gross Profit 6056.36%
Depreciation & Amortization $193.0K $241.0K $517.0K
YoY Change -72.78% -53.38% 353.51%
% of Gross Profit 940.0%
Operating Expenses $6.169M $10.57M $11.69M
YoY Change -56.18% -9.58% 45.45%
Operating Profit -$6.169M -$10.57M -$11.64M
YoY Change -49.36% -9.15% 54.25%
Interest Expense $35.00K $31.00K $328.0K
YoY Change -86.0% -90.55% -46.67%
% of Operating Profit
Other Income/Expense, Net -$110.0K $10.00K -$282.0K
YoY Change -47.12% -103.55% -51.88%
Pretax Income -$6.279M -$10.56M -$11.92M
YoY Change -49.32% -11.38% 46.6%
Income Tax $0.00 $0.00 $0.00
% Of Pretax Income
Net Earnings -$7.294M -$33.55M -$11.92M
YoY Change -41.13% 181.48% 46.64%
Net Earnings / Revenue -7095.24%
Basic Earnings Per Share -$0.55 -$3.42 -$1.37
Diluted Earnings Per Share -$0.55 -$3.42 -$54.68K
COMMON SHARES
Basic Shares Outstanding 12.37M 9.007M 218.0M
Diluted Shares Outstanding 13.18M 9.807M 8.715M

Balance Sheet

Concept 2023 Q1 2022 Q4 2021 Q4
SHORT-TERM ASSETS
Cash & Short-Term Investments $6.562M $9.625M $57.38M
YoY Change -86.49% -83.22% -46.7%
Cash & Equivalents $6.562M $3.696M $39.21M
Short-Term Investments $0.00 $5.929M $18.17M
Other Short-Term Assets $19.29M $20.87M $2.922M
YoY Change 721.16% 614.27% 51.24%
Inventory $1.986M
Prepaid Expenses
Receivables $70.00K $970.0K $111.0K
Other Receivables $1.172M $6.769M $5.100M
Total Short-Term Assets $27.09M $38.24M $67.50M
YoY Change -55.08% -43.35% -39.13%
LONG-TERM ASSETS
Property, Plant & Equipment $4.358M $4.245M $32.88M
YoY Change -87.4% -87.09% 556.31%
Goodwill
YoY Change
Intangibles $5.393M $5.398M $5.041M
YoY Change 9.64% 7.08% 325.4%
Long-Term Investments $1.760M
YoY Change
Other Assets $3.303M $303.0K $7.176M
YoY Change -52.44% -95.78% 29800.0%
Total Long-Term Assets $17.29M $13.57M $58.95M
YoY Change -68.59% -76.99% 70.76%
TOTAL ASSETS
Total Short-Term Assets $27.09M $38.24M $67.50M
Total Long-Term Assets $17.29M $13.57M $58.95M
Total Assets $44.39M $51.80M $126.4M
YoY Change -61.53% -59.03% -13.04%
SHORT-TERM LIABILITIES
YoY Change
Accounts Payable $2.013M $4.002M $2.739M
YoY Change -61.87% 46.11% 13.7%
Accrued Expenses $3.821M $5.001M $2.842M
YoY Change 30.01% 75.97% 68.87%
Deferred Revenue
YoY Change
Short-Term Debt $13.70M $16.01M $0.00
YoY Change -100.0%
Long-Term Debt Due $421.0K $411.0K $45.00K
YoY Change 835.56% 813.33% -85.58%
Total Short-Term Liabilities $22.28M $27.78M $8.346M
YoY Change 169.0% 232.79% 36.77%
LONG-TERM LIABILITIES
Long-Term Debt $1.496M $1.878M $22.10M
YoY Change -93.25% -91.5% -30.89%
Other Long-Term Liabilities $3.225M $3.324M $5.400M
YoY Change -41.87% -38.44%
Total Long-Term Liabilities $4.721M $5.202M $27.50M
YoY Change -82.96% -81.09% -14.0%
TOTAL LIABILITIES
Total Short-Term Liabilities $22.28M $27.78M $8.346M
Total Long-Term Liabilities $4.721M $5.202M $27.50M
Total Liabilities $27.00M $32.98M $35.85M
YoY Change -24.99% -8.01% -5.83%
SHAREHOLDERS EQUITY
Retained Earnings -$282.9M -$275.6M -$194.5M
YoY Change 36.75% 41.71%
Common Stock $300.3M $294.6M $285.2M
YoY Change 4.83% 3.31%
Preferred Stock
YoY Change
Treasury Stock (at cost)
YoY Change
Treasury Stock Shares
Shareholders Equity $17.39M $18.82M $90.60M
YoY Change
Total Liabilities & Shareholders Equity $44.39M $51.80M $126.4M
YoY Change -61.53% -59.03% -13.04%

Cashflow Statement

Concept 2023 Q1 2022 Q4 2021 Q4
OPERATING ACTIVITIES
Net Income -$7.294M -$33.55M -$11.92M
YoY Change -41.13% 181.48% 46.64%
Depreciation, Depletion And Amortization $193.0K $241.0K $517.0K
YoY Change -72.78% -53.38% 353.51%
Cash From Operating Activities -$3.872M -$7.635M -$10.69M
YoY Change -34.86% -28.55% 26.93%
INVESTING ACTIVITIES
Capital Expenditures -$201.0K $2.954M -$2.133M
YoY Change -90.32% -238.49% 111.82%
Acquisitions
YoY Change
Other Investing Activities $8.040M $1.699M $1.161M
YoY Change 209.95% 46.34% -109.72%
Cash From Investing Activities $8.241M -$1.255M -$972.0K
YoY Change 1494.0% 29.12% -92.5%
FINANCING ACTIVITIES
Cash Dividend Paid
YoY Change
Common Stock Issuance & Retirement, Net
YoY Change
Debt Paid & Issued, Net
YoY Change
Cash From Financing Activities 1.464M -3.044M -6.017M
YoY Change -14740.0% -49.41% -117.15%
NET CHANGE
Cash From Operating Activities -3.872M -7.635M -10.69M
Cash From Investing Activities 8.241M -1.255M -972.0K
Cash From Financing Activities 1.464M -3.044M -6.017M
Net Change In Cash 5.833M -11.93M -17.68M
YoY Change -207.28% -32.48% -228.93%
FREE CASH FLOW
Cash From Operating Activities -$3.872M -$7.635M -$10.69M
Capital Expenditures -$201.0K $2.954M -$2.133M
Free Cash Flow -$3.671M -$10.59M -$8.553M
YoY Change -5.07% 23.8% 15.39%

Facts In Submission

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<p style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-weight:bold;margin:0pt 0pt 10pt 0pt;">1.   Nature of Business </p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt 0pt 10pt 0pt;">iBio, Inc. (“we”, “us”, “our”, “iBio”, “iBio, Inc” or the “Company”) is an Artificial Intelligence (“AI”)-driven innovator of precision antibody immunotherapies. The Company has a pipeline of innovative primarily immuno-oncology antibodies against hard-to-drug targets where we may face reduced competition and with antibodies that may be more selective. The Company plans to use its AI-driven discovery platform to continue adding antibodies against hard-to-drug targets or to work with partners on AI-driven drug development. </p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt 0pt 10pt 0pt;"><b style="font-weight:bold;">Therapeutics Pipeline </b></p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt 0pt 10pt 0pt;"><img alt="Graphic" src="ibio-20221231x10q001.jpg" style="display:inline-block;height:219.9pt;left:0%;padding-bottom:0.6pt;position:relative;top:0pt;width:540pt;"/></p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt;"><b style="font-weight:bold;">IBIO-101: </b>an anti-CD25 molecule that works by depletion of immunosuppressive T-regulatory cells (“Tregs”) via antibody-dependent cellular cytotoxicity (“ADCC”), without disrupting activation of effector T-cells (“Teffs”) in the tumor microenvironment. IBIO-101 could potentially be used to treat solid tumors, hairy cell leukemia, relapsed multiple myeloma, lymphoma, or head and neck cancer. IBIO-101 is currently in the Investigational New Drug (“IND”) enabling stage. We have contracted with a contract research organization (CRO) to assist with the development of the manufacturing process, which includes but not limited to process and cell line development for the production of the drug substance and drug product. As we continue with the development of the manufacturing process for IBIO-101, as a fast-follower to a competing drug candidate, we have decided to pause the IND enabling studies until our competitor releases clinical data. Due to the decision to pause the IND enabling studies, we expect the IND filing for IBIO-101 will be delayed from the first half of 2024 to the first half of 2025. This delay will allow us to thoroughly evaluate the market potential and optimize our financial resources and the development plan for IBIO-101 to maximize its potential for success.</p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt;"><span style="visibility:hidden;">​</span></p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt;"><b style="font-weight:bold;">EGFRvIII: </b>binds a tumor-specific mutation of EGFR variant III with an afucosylated antibody for high ADCC. Because of its specificity binding to the tumor-specific mutation, it could potentially reduce toxicity and/or expand the therapeutic window compared to simple broad EGFR-targeted alternatives. EGFRvIII is constantly “switched on” which can lead to the development of a range of different cancers. An EGFRvIII antibody could potentially be used to treat glioblastoma, head and neck cancer or non-small cell lung cancer.</p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt;"><span style="visibility:hidden;">​</span></p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt;"><b style="font-weight:bold;">CCR8:</b> targets depletion of highly immunosuppressive CCR8+ Tregs in the tumor microenvironment via an ADCC mechanism with selective binding to CCR8 over its closely related cousin, CCR4, to avoid off-target effects. A CCR8 program could potentially be broadly applicable in solid tumors and/or as a prospective combination therapy.</p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt;"><span style="visibility:hidden;">​</span></p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt;"><b style="font-weight:bold;">MUC16:</b> a highly expressed target on ovarian cancer cells and an attractive tumor associated target for therapeutic antibodies. However, antibodies targeting MUC16 are prone to tumor resistance via epitope shedding and dysregulated glycosylation. Epitope-steered antibodies that bind to an epitope that avoids both of these tumor resistance mechanisms could potentially be used to treat MUC16 positive tumors, particularly those tumors that are resistant to other MUC16 antibodies.</p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt;"><span style="font-weight:bold;visibility:hidden;">​</span></p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt;"><b style="font-weight:bold;">PD-1 Agonist: </b>selectively binds PD-1 to suppress auto-reactive T-cells without PD-L1/PD-L2 blocking. A PD-1 agonist could potentially be used to treat inflammatory bowel disease, systemic lupus erythematosus, multiple sclerosis or other inflammatory diseases.</p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt;"><span style="font-weight:bold;visibility:hidden;">​</span></p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt;">In addition to the programs described above, the Company also has five additional early discovery programs that have the potential to advance into later stages of preclinical development and are designed to tackle hard-to-drug targets.</p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt;"><span style="visibility:hidden;">​</span></p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;line-height:1.26;margin:0pt 0pt 12pt 0pt;"><b style="font-weight:bold;">IBIO-100 and Endostatin E4</b></p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt;">Our preclinical anti-fibrotic program, IBIO-100, has been undergoing a review process as part of our ongoing effort to prioritize our resources and focus on the most promising opportunities. The IBIO-100 program design is based in part upon work by Dr. Carol Feghali-Bostwick, Professor of Medicine at the Medical University of South Carolina and Vice-Chair of the Scleroderma Foundation. Her initial work was conducted at the University of Pittsburgh, and we have licensed the patents relevant for the continued development of the molecule from the university.  After careful consideration, we have decided to terminate all efforts on IBIO-100 anti-fibrotic program and to cancel the license agreement with the University of Pittsburgh. The lead optimization and manufacturing of IBIO-100 have proven to be very challenging, and we will continue to prioritize our resources to fit into our immune-oncology monoclonal antibody strategy. </p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt;"><span style="visibility:hidden;">​</span></p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt;">As part of this decision, we are intending to complete the pre-clinical cancer studies we are conducting in collaboration with University of Texas Southwestern using E4 endostatin peptide, which is derived from IBIO-100. After the pre-clinical studies are completed, we will re-assess whether to further pursue the oncology program and have further discussions with the University of Pittsburgh. This approach allows us to gather valuable data and insights that will inform our future decisions regarding the potential of E4 endostatin peptide as an oncology program. </p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt;"><span style="visibility:hidden;">​</span></p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt 0pt 10pt 0pt;"><b style="font-weight:bold;">AI Drug Discovery Platform</b></p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt 0pt 10pt 0pt;">In September 2022, the Company purchased substantially all of the assets of RubrYc Therapeutics (for a complete description of the transaction please see Note 6 – Significant Transactions). The AI Drug Discovery platform technology is designed to be used to discover antibodies that bind to hard-to-target subdominant and conformational epitopes for further development within our existing portfolio or in partnership with outside entities. The RubrYc AI platform is built upon 3 key technologies. </p><table style="border-collapse:collapse;font-family:'Times New Roman','Times','serif';font-size:10pt;margin-bottom:10pt;margin-top:0pt;table-layout:fixed;text-align:justify;width:100%;border:0pt;"><tr><td style="width:18pt;"/><td style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-weight:bold;vertical-align:text-top;white-space:nowrap;width:18pt;padding:0pt;">1.</td><td style="padding:0pt;"><b style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-style:normal;font-weight:bold;">Epitope Targeting Engine:</b><span style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-style:normal;font-weight:normal;"> A patented machine-learning platform that combines computational biology and 3D-modeling to identify molecules that mimic hard-to-target binding sites on target proteins, specifically, </span><span style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-style:normal;font-weight:normal;">subdominant and conformational epitopes.</span><span style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-style:normal;font-weight:normal;"> </span><span style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-style:normal;font-weight:normal;">The creation of these small mimics enables the engineering of therapeutic antibody candidates that can selectively bind immune and cancer cells better than ”trial and error” antibody engineering and screening methods that are traditionally focused on dominant epitopes.</span><span style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-style:normal;font-weight:normal;"> </span></td></tr></table><table style="border-collapse:collapse;font-family:'Times New Roman','Times','serif';font-size:10pt;margin-bottom:10pt;margin-top:0pt;table-layout:fixed;text-align:justify;width:100%;border:0pt;"><tr><td style="width:18pt;"/><td style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-weight:bold;vertical-align:text-top;white-space:nowrap;width:18pt;padding:0pt;">2.</td><td style="padding:0pt;"><span style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-style:italic;font-weight:bold;">RubrYcHu</span><sup style="font-family:'Times New Roman','Times','serif';font-size:7.5pt;font-style:normal;font-weight:bold;line-height:100%;top:0pt;vertical-align:top;">TM</sup><b style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-style:normal;font-weight:bold;"> Library:</b><span style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-style:normal;font-weight:normal;"> An </span><span style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-style:normal;font-weight:normal;">AI-generated human antibody library free of significant sequence liabilities that provides a unique pool of antibodies to screen. </span><span style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-style:normal;font-weight:normal;">The combination of the Epitope Targeting Engine and screening with the </span><i style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-style:italic;font-weight:normal;">RubrYcHu</i><span style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-style:normal;font-weight:normal;"> Library has been shown to reduce the discovery time from ideation to </span><i style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-style:italic;font-weight:normal;">in vivo</i><span style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-style:normal;font-weight:normal;"> proof-of-concept (PoC) by up to 4 months. This has the potential to enable more, and better, therapeutic candidates to reach the clinic, faster. </span></td></tr></table><table style="border-collapse:collapse;font-family:'Times New Roman','Times','serif';font-size:10pt;margin-bottom:0pt;margin-top:0pt;table-layout:fixed;text-align:justify;width:100%;border:0pt;"><tr><td style="width:18pt;"/><td style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-weight:bold;vertical-align:text-top;white-space:nowrap;width:18pt;padding:0pt;">3.</td><td style="padding:0pt;"><span style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-style:italic;font-weight:bold;">StableHu</span><sup style="font-family:'Times New Roman','Times','serif';font-size:7.5pt;font-style:normal;font-weight:bold;line-height:100%;top:0pt;vertical-align:top;">TM</sup><b style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-style:normal;font-weight:bold;"> Library:</b><span style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-style:normal;font-weight:normal;"> An </span><span style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-style:normal;font-weight:normal;">AI-powered sequence optimization library used to improve antibody performance</span><span style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-style:normal;font-weight:normal;">. Once an antibody has been advanced to the lead optimization stage, </span><i style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-style:italic;font-weight:normal;">StableHu</i><span style="font-family:'Times New Roman','Times','serif';font-size:10pt;font-style:normal;font-weight:normal;"> allows precise and rapid optimization of the antibody binding regions to rapidly move a candidate molecule into the IND-enabling stage.</span></td></tr></table><div style="margin-top:10pt;"/><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt 0pt 10pt 0pt;">On January 3, 2023, the United States Patent and Trademark Office issued U.S. Patent No. 11,545,238, entitled “Machine Learning Method for Protein Modelling to Design Engineered Peptides,” which, among other claims, covers a machine learning model for engineering peptides, including antibody epitope therapeutics.  Subject to any potential patent term extensions, the patent will expire on May 13, 2040.</p>
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<p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt 0pt 10pt 0pt;"><i style="font-style:italic;">Concentrations of Credit Risk</i></p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt 0pt 10pt 0pt;"><span style="text-decoration-color:#000000;text-decoration-line:underline;text-decoration-style:solid;">Cash</span></p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt 0pt 10pt 0pt;">The Company maintains principally all cash balances in two financial institutions which, at times, may exceed the insured amounts. The exposure to the Company is solely dependent upon daily balances and the strength of the financial institutions. The Company has not incurred any losses on these accounts. At December 31, 2022 and June 30, 2022, amounts in excess of insured limits were approximately $3,400,000 and $18,200,000, respectively. </p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt 0pt 10pt 0pt;"><span style="text-decoration-color:#000000;text-decoration-line:underline;text-decoration-style:solid;">Revenue</span></p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt;">During the three months ended December 31, 2022, the Company reported no revenue from continuing operations and generated 100% of its revenue reported in discontinued operations from two customers. During the three months ended December 31, 2021, the Company reported no revenue from continuing operations and generated 100% of its revenue reported in discontinued operations from four customers.</p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt;"><span style="visibility:hidden;">​</span></p><p style="font-family:'Times New Roman','Times','serif';font-size:10pt;text-align:justify;margin:0pt;">During the six months ended December 31, 2022, the Company reported no revenue from continuing operations and generated 100% of its revenue reported in discontinued operations from three customers. During the six months ended December 31, 2021, the Company reported revenue from continuing operations from one customer related to the settlement of a revenue contract and generated 100% of revenue reported in discontinued operations from four customers.</p>
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CY2021Q4 ibio Unconsolidated Gain Loss Attributable To Stockholders
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