Inhibikase Therapeutics (IKT) Operating Expenses (2020 - 2026)
Inhibikase Therapeutics (IKT) posted Operating Expenses of $21.05 million for Q2 2026, up 94.3% from $10.83 million a year earlier and up 18.0% from the prior quarter.
Inhibikase Therapeutics (IKT) Operating Expenses (2020 - 2026) Analysis & Trends
For the trailing twelve months through Jun 30, 2026, Operating Expenses at Inhibikase Therapeutics was $65.44 million, up 48.1% year-over-year; for FY2025, it was $51.97 million, up 81.8% from FY2024.
- Annual Operating Expenses has increased for five consecutive years, with a five-year compound annual growth rate of 71.4% (FY2020 to FY2025).
- In prior years, Inhibikase Therapeutics' Operating Expenses was $28.59 million in FY2024 (+40.5%), $20.35 million in FY2023 (+11.5%), $18.25 million in FY2022 (+2.2%) and $17.87 million in FY2021 (+408.0%).
- The Q2 2026 figure stands as the highest quarterly Operating Expenses in data going back to Q1 2020.
- On a year-over-year basis, Operating Expenses has increased in each of the last eight quarters, with growth averaging 97.0% over the last eight quarters.
- The strongest year-over-year quarter for Operating Expenses in the past five years was Q3 2021, with growth of 584.3%; the weakest was Q2 2024, with a decline of 20.1%.
- According to Business Quant data, Operating Expenses for the three prior quarters was $17.84 million (Q1 2026), $13.78 million (Q4 2025) and $12.77 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Eli Lilly | 1,136.97 Bn | 1,105.55 Bn | 19.71 Bn | 7.95 Bn |
| 2 | Johnson & Johnson | 619.72 Bn | 538.25 Bn | 17.26 Bn | 10.12 Bn |
| 3 | AbbVie | 503.40 Bn | 476.52 Bn | 12.70 Bn | 10.56 Bn |
| 4 | Merck | 370.15 Bn | 324.53 Bn | 12.21 Bn | 12.80 Bn |
| 5 | Novartis Ag | 298.64 Bn | 262.63 Bn | 11.24 Bn | 6.09 Bn |
| 6 | Astrazeneca | 261.07 Bn | 234.76 Bn | 12.86 Bn | 9.70 Bn |
| 7 | Amgen | 237.00 Bn | 192.40 Bn | 7.24 Bn | 6.54 Bn |
| 8 | Gilead Sciences | 191.24 Bn | 165.43 Bn | 6.22 Bn | 18.20 Bn |
| 9 | Pfizer | 170.31 Bn | 116.96 Bn | 10.94 Bn | 6.68 Bn |
| 10 | Inhibikase Therapeutics | 288.92 Mn | -296.56 Mn | - | 21.05 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 21.05 Mn |
| Mar 31, 2026 | 17.84 Mn |
| Dec 31, 2025 | 13.78 Mn |
| Sep 30, 2025 | 12.77 Mn |
| Jun 30, 2025 | 10.83 Mn |
| Mar 31, 2025 | 14.60 Mn |
| Dec 31, 2024 | 12.93 Mn |
| Sep 30, 2024 | 5.83 Mn |
| Jun 30, 2024 | 5.05 Mn |
| Mar 31, 2024 | 4.78 Mn |
| Dec 31, 2023 | 4.40 Mn |
| Sep 30, 2023 | 4.85 Mn |
| Jun 30, 2023 | 6.32 Mn |
| Mar 31, 2023 | 4.78 Mn |
| Dec 31, 2022 | 4.40 Mn |
| Sep 30, 2022 | 4.52 Mn |
| Jun 30, 2022 | 4.65 Mn |
| Mar 31, 2022 | 4.69 Mn |
| Dec 31, 2021 | 5.04 Mn |
| Sep 30, 2021 | 4.80 Mn |
Inhibikase Therapeutics 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=IKT&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "IKT", "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=IKT&period=max&api_key=YOUR_API_KEY");
const data = await res.json();