What are the major steps in the process of drug discovery?
The major steps in drug discovery include early drug discovery where potential leads are identified and optimized, pre-clinical trials involving laboratory testing and optimization of these substances, clinical trials divided into four phases assessing safety, dosage, efficacy, and interactions in progressively larger patient groups, and finally regulatory approval where aggregated clinical data is submitted for authorization before the drug can be marketed.
There are four major steps in the drug discovery process: early drug discovery, pre-clinical phase, clinical phase, and regulatory approval.
Phase 1: Early drug discovery
The early drug discovery phase uses many varying actions and tests, with researchers collaborating to identify and optimize prospected leads to a specific target.
Phase 2: Pre-clinical trial
In this phase, identified substances from phase 1 are tested and optimized in a laboratory setting and in other relevant formats.
Phase 3: Clinical trial
There are four phases of a clinical trial, I-IV.
-
Phase 1: Small groups of healthy subjects will be used to study the tolerance and safety of the drug candidate, aiming to answer three questions:
- 1.Is the drug safe?
- 2.What doses can be administered without side effects?
- 3.How does the substance behave in the body?
-
Phase 2: This phase gets further subdivided into 2 stages, using a slightly large group of patients, typically between 100 - 500 adult patients:
- Phase 2a: Therapy concept is checked
- Phase 2b: The aim is to find the right dose
-
Phase 3: Doctors test the drug on large groups of people to assess the effectiveness across many different patients. These tests not only confirm efficacy across a statistically significant group but also indicate potential interactions with other drugs.
Phase 4: Regulatory approval
After completing clinical trials, data can then be aggregated, analyzed, and submitted to authorities as appropriate. Prior to sale and consumption, drugs require approval from a suitable regulatory authority.
Related
Agility, Flexibility, and Collaboration: The Three Principles That Redefined Post-Pandemic R&D
The COVID-19 pandemic permanently transformed R&D by establishing agility through AI and faster learning cycles, flexibility via adaptive trial methods, and enhanced collaboration through open data and public-private partnerships, setting a new standard for rapid, rigorous, and cooperative scientific innovation in healthcare.
Our 11 Most-Read Blogs of 2023: AI, Biologics, ELNs, and More
Dotmatics' top blogs of 2023 highlight key scientific advancements including the convergence of small molecule and biologic drug development, the integration and comparison of LIMS and ELN systems for enhanced lab data management, and strategies to minimize human errors in biologics research through automation and calibration.
Lessons Learned for AI in Small Molecule Drug Discovery
The article discusses the longstanding use of machine learning in small molecule drug discovery, highlights the recent surge of AI-driven start-ups amid industry hype, and emphasizes the need to temper expectations by learning from past technology challenges and other industries to successfully implement AI in drug discovery.
The Future of Biologics Discovery
The article discusses the transformative potential of biologics in medicine, highlighting that biologics discovery involves designing complex, biologically-derived drugs like antibodies and gene therapies, which require sophisticated technology and controlled conditions due to their sensitivity and complexity, while also addressing challenges such as workflow complexity, high costs, and safety concerns in the development process.
Webinar: The New Era of Small Molecule Drug Discovery
The webinar on August 18 at 4pm BST/11am EDT, featuring Haydn Boehm—an experienced organic chemist and life science industry leader—will address the challenges in small molecule drug discovery and demonstrate strategies to reduce risks, costs, and time to accelerate therapeutic breakthroughs.
3 Customer Trends We’re Watching in 2025
In 2025, life science teams are prioritizing three key trends—Lab-in-a-Loop platforms that integrate instruments, data, workflows, and models to boost R&D efficiency; true multimodal discovery enabled by flexible informatics supporting diverse data types without fragmented tools; and Composite AI leveraging layered, governed, and traceable data across disciplines—all aimed at delivering tangible scientific innovation while addressing resource constraints and stringent AI governance requirements.