Erasca (ERAS) Operating Expenses (2020 - 2026)
Erasca (ERAS) recorded Operating Expenses of $47.62 million in Q2 2026, up 24.9% from $38.13 million a year earlier but down 74.7% from the prior quarter.
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Analysis
Erasca (ERAS) Operating Expenses (2020 - 2026) Analysis & Trends
On a TTM basis, Erasca's Operating Expenses came in at $302.68 million as of Jun 30, 2026, up 106.3% year-over-year; for FY2025, it was $140.91 million, down 21.5% from FY2024.
- Annual Operating Expenses has a five-year compound annual growth rate of 5.2% (FY2020 to FY2025).
- Across earlier years, Operating Expenses came in at $179.59 million in FY2024 (+26.9%), $141.53 million in FY2023 (-42.8%), $247.45 million in FY2022 (+98.1%) and $124.88 million in FY2021 (+14.3%).
- Quarterly Operating Expenses has ranged from $31.05 million in Q4 2021 to $187.91 million in Q1 2026 over the past five years.
- On a year-over-year basis, Operating Expenses rose in four of the last eight quarters, with growth averaging 49.6%.
- Peak year-over-year performance for Operating Expenses in the last five years was growth of 427.4% in Q1 2026, against a decline of 75.5% in Q4 2023 at the low end.
- Per Business Quant, the preceding three quarters came in at $187.91 million (Q1 2026), $32.6 million (Q4 2025) and $34.55 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Johnson & Johnson | 655.56 Bn | 574.09 Bn | 17.26 Bn | 10.12 Bn |
| 2 | AbbVie | 470.94 Bn | 444.10 Bn | 12.70 Bn | 10.56 Bn |
| 3 | Merck | 367.14 Bn | 321.57 Bn | 12.21 Bn | 12.80 Bn |
| 4 | Novartis Ag | 280.33 Bn | 236.20 Bn | 11.24 Bn | -6.09 Bn |
| 5 | Astrazeneca | 257.78 Bn | 231.34 Bn | 12.86 Bn | -9.70 Bn |
| 6 | Amgen | 226.04 Bn | 181.44 Bn | 7.24 Bn | 6.54 Bn |
| 7 | Gilead Sciences | 188.89 Bn | 163.01 Bn | 6.22 Bn | 18.20 Bn |
| 8 | Pfizer | 163.75 Bn | 110.70 Bn | 10.94 Bn | 6.68 Bn |
| 9 | Vertex Pharmaceuticals | 133.68 Bn | 105.69 Bn | 2.84 Bn | 2.09 Bn |
| 10 | Erasca | 4.42 Bn | 4.42 Bn | - | 47.62 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 47.62 Mn |
| Mar 31, 2026 | 187.91 Mn |
| Dec 31, 2025 | 32.60 Mn |
| Sep 30, 2025 | 34.55 Mn |
| Jun 30, 2025 | 38.13 Mn |
| Mar 31, 2025 | 35.63 Mn |
| Dec 31, 2024 | 35.71 Mn |
| Sep 30, 2024 | 37.24 Mn |
| Jun 30, 2024 | 67.78 Mn |
| Mar 31, 2024 | 38.85 Mn |
| Dec 31, 2023 | 33.87 Mn |
| Sep 30, 2023 | 34.66 Mn |
| Jun 30, 2023 | 35.97 Mn |
| Mar 31, 2023 | 37.03 Mn |
| Dec 31, 2022 | 138.08 Mn |
| Sep 30, 2022 | 36.96 Mn |
| Jun 30, 2022 | 35.91 Mn |
| Mar 31, 2022 | 36.51 Mn |
| Dec 31, 2021 | 31.05 Mn |
| Sep 30, 2021 | 46.04 Mn |
API Access
Erasca Operating Expenses API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=operating-expenses&ticker=ERAS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "ERAS", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=operating-expenses&ticker=ERAS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();