C4 Therapeutics, Inc. · NASDAQ:CCCC

C4 Therapeutics — World Pharma Week 2020

22 slides from C4 Therapeutics's conference presentation, Dec 10, 2020

1. Slide 1: Understanding the Nuances of Targeted Protein Degradation

   Understanding the Nuances of Targeted Protein Degradation A new class of small-molecule protein degraders harnessing powerful and universal cellular biology Stewart L. Fisher Webinar December 10, 2020 © 2020 C4 THERAPEUTICS, INC.

2. Slide 2: Forward-looking Statements and Intellectual Property

   Forward-looking Statements and Intellectual Property Forward-looking Statements The following presentation contains forward-looking statements. All statements other than statements of historical fact are forward- looking statements, which are often indicated by terms such as “anticipate,” “believe,”

3. Slide 3: Degraders Enable a Catalytic Cycle that Destroys Disease-Relevant Proteins

   Degraders Enable a Catalytic Cycle that Destroys Disease-Relevant Proteins © 2020 C4 THERAPEUTICS, INC. 3

4. Slide 4: Protein Degradation is Fundamentally Different than Protein Inhibition

   Protein Degradation is Fundamentally Different than Protein Inhibition Protein degraders allow for a more potent and durable pharmacological response at lower overall exposure levels than inhibitors Key Advantages of Protein Degraders 1. Improved Potency Degraders are recycled and can engage multipl

5. Slide 5: TORPEDO Platform: Robust Drug Discovery and Higher Confidence in Clinical Outcomes

   TORPEDO Platform: Robust Drug Discovery and Higher Confidence in Clinical Outcomes Design Analyze Predict Predicted ternary HDX-MS complexes • Computational method incorporates • Cellular degradation data fitted using an • Universal modeling framework merges experimental data to identify top models

6. Slide 6: The TORPEDO Platform Employs Cutting Edge Structure-based Design

   The TORPEDO Platform Employs Cutting Edge Structure-based Design X-RAY CRYSTALLOGRAPHY HYDROGEN-DEUTERIUM EXCHANGE MASS SPEC (HDX-MS) Ligase Target • >70 proprietary X-ray structures solved • Established in 2018 via open access labs at UMass Amherst • Coverage includes key E3 ligases, ~70% of target

7. Slide 7: Incorporation of Solution State Data is Critical for Predictive Ternary Complex Models

   Incorporation of Solution State Data is Critical for Predictive Ternary Complex Models in silico modeling of BRD4 degraders Ternary complex ranking using HDX-MS Data Utilize HDX-MS Surface Mapping as Constraints Degrader Only Remove BRD4 Steric Clashes Virtual Docking with CRBN Rank Conformers on HD

8. Slide 8: Degraders are Essential Catalytic Activators

   Degraders are Essential Catalytic Activators D ternary complex T formation T E3 E3 U U U U U T E2 proteasome- mediated complex Ub-charged E2 degrader recruitment degradation dissociation acts as an U essential T U U U activator of U T E3 T E2 E3 E3 processive Ub- Ub-transfer to chain elongation targ

9. Slide 9: Cellular Degradation is Time Dependent

   Cellular Degradation is Time Dependent Single Timepoint Time Course 120 100 % Protein Remaining 80 DC50 60 Emax 40 Time 20 0 1 nM 10 nM 100 nM 0.001 nM 0.003 nM 0.01 nM 0.032 nM 0.1 nM 0.316 nM 3.162 nM 1000 nM 10000 nM 31.623 nM 316.228 nM 3162.278 nM DC50 – [degrader] for 50% target depletion (≈ c

10. Slide 10: BiDAC Degradation Activity is Not Strictly Dependent on Target Binding Affinity

    BiDAC Degradation Activity is Not Strictly Dependent on Target Binding Affinity Degradation Potency 10 Degradation Depth 10 (DC50, nM) (Emax, %) 1 1 BRD9 DC50 BRD9 Emax BRD4 DC50 BRD4 Emax 0.1 0.1 100 1000 100 1000 Target Binding Affinity Target Binding Affinity (KI , nM) (KI , nM) • Excellent degra

11. Slide 11: Prevailing Assumption: Ternary Complex Formation Drives Potency

    Prevailing Assumption: Ternary Complex Formation Drives Potency D ternary complex T formation T E3 fast E3 U U U U U proteasome- T E2 Implications: mediated complex Ub-charged E2 fast fast degrader degradation dissociation acts as an recruitment • Ternary complex Ktf = KM essential T U U U U activat

12. Slide 12: What about Kinetically-driven Systems?

    What about Kinetically-driven Systems? D T med Implications: T • No correlations between ternary E3 “slow” E3 complex formation and catalysis U U U U U E2 • Negative binding cooperativity T proteasome- mediated complex fast tolerated fast degrader degradation dissociation acts as an essential U T U

13. Slide 13: Applying an Enzymology Framework Provides Quantitative Assessments of Degrader Activity

    Applying an Enzymology Framework Provides Quantitative Assessments of Degrader Activity Vmax E2 T U T Kpc U U U U U U U U D T T T E2 Ubiqitination U E3 E3 E2 E3 U E2 120 Excess degrader “hook effect” Ki 100 % Protein Remaining 80 degraded target protein T 60 Kpc U 40 E2 KI E3 20 0 Emax ∝ Vmax: (kact

14. Slide 14: Proprietary PK/PD Models Founded on Degradation Enzymology Framework

    Proprietary PK/PD Models Founded on Degradation Enzymology Framework Enzymology Framework 120 % Protein Remaining 100 80 60 PREDICTION 40 20 Time 0 10 nM 0.01 nM 0.001 nM 100 nM 0.1 nM 1 nM 1000 nM 10000 nM C4T Proprietary Exposure TIME DEPENDENCE PK/PD Models 120 % Protein Remaining 100 80 Time 60

15. Slide 15: PK/PD Models Provide Robust Predictions Across the Diverse Targets and Degrader Classes

    PK/PD Models Provide Robust Predictions Across the Diverse Targets and Degrader Classes 100 1.5 100 5000 Plasma PK (ng/ml) Plasma PK (ng/ml) 4000 75 75 [BRD9] (%) [IKZFZ1] % 1.0 3000 50 50 2000 0.5 25 25 1000 0 0.0 0 0 0 6 12 18 24 0 6 12 18 24 30 36 Time (hr) Time (hr) CFT7455, 1 mg/kg dose po, KI-

16. Slide 16: PK/PD Models Provide Robust Predictions Across the Diverse Targets and Degrader Classes

    PK/PD Models Provide Robust Predictions Across the Diverse Targets and Degrader Classes 100 1.5 100 5000 Predicted PD Predicted PD Plasma PK (ng/ml) Plasma PK (ng/ml) 4000 75 75 Plasma PK Plasma PK [BRD9] (%) [IKZFZ1] % 1.0 3000 50 50 2000 0.5 25 25 1000 0 0.0 0 0 0 6 12 18 24 0 6 12 18 24 30 36 Tim

17. Slide 17: PK/PD Models Provide Robust Predictions Across the Diverse Targets and Degrader Classes

    PK/PD Models Provide Robust Predictions Across the Diverse Targets and Degrader Classes 100 1.5 100 5000 Predicted PD Predicted PD Plasma PK (ng/ml) Plasma PK (ng/ml) Observed PD Observed PD 4000 75 75 Plasma PK Plasma PK [BRD9] (%) [IKZFZ1] % 1.0 3000 50 50 2000 0.5 25 25 1000 0 0.0 0 0 0 6 12 18 2

18. Slide 18: Pharmacodynamic Threshold: Linking Pharmacodynamics to Efficacy

    Pharmacodynamic Threshold: Linking Pharmacodynamics to Efficacy ASCENDING DOSE PK PHARMACODYNAMIC RESPONSE DOSE RESPONSE EFFICACY 150 100 800 Tumor Volume (mm3) 125 75 600 100 [Target] % [Drug] 75 50 400 50 25 200 Desired DC50 25 Efficacious 0 0 0 Response 0 10 20 30 40 50 60 0 10 20 30 40 50 60 0 5

19. Slide 19: Degrader Catalytic Activity Drives Pharmacology: Lower Exposures Required

    Degrader Catalytic Activity Drives Pharmacology: Lower Exposures Required REVERSIBLE INHIBITOR COVALENT INHIBITOR DEGRADER 100 100 100 AUC PDT = !!"#$% AUC ! PDT = "#$% AUC PDT = "& IC50 '$ 75 75 75 [Drug] [Drug] [Drug] 50 50 50 IC50 IC50 DC50 25 25 25 0 0 0 0 10 20 30 40 50 60 0 10 20 30 40 50 60 0

20. Slide 20: Degrader Pharmacodynamic Driver Landscape

    Degrader Pharmacodynamic Driver Landscape POOR DEGRADER GOOD DEGRADER EXCELLENT DEGRADER 100 100 100 𝐴𝑈𝐶 !#$% PDT = PDT = AUC PDT = 𝑘%$& &AUC 75 𝐾#$ kact << Kpc 75 "'$ 75 kact >> Kpc [Drug] [Drug] [Drug] 50 50 50 IC50 DC50 DC50 25 25 25 0 0 0 0 10 20 30 40 50 60 0 10 20 30 40 50 60 0 10 20 30…

21. Slide 21: Potential Advantages of Degraders over Inhibitors

    Potential Advantages of Degraders over Inhibitors • PDT analysis assumes equivalent pharmacological response for degraders and inhibitors • Degraders are expected to exhibit one or more of the following attributes over inhibitors (target dependent): − Prolonged activity duration due to target resynt

22. Slide 22: The C4 Therapeutics Team

    The C4 Therapeutics Team © 2020 C4 THERAPEUTICS, INC. 22

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