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Quantum Computing In Pharmacological Studies: Clinical Trials & Their Significance

Updated: Dec 20, 2022

The use of quantum computing as a tool for facilitating drug design has sprouted interest within several domains of the biotechnology industry. In this series, we will explore a more pressing need that quantum computing can fulfill- clinical trials.

The journey of making a drug requires multiple phases of feasibility and omics studies before it is applied to a sample population of non-human trials. Once these have been successful, the drug is then elevated to human trials to test for secondary reactions and side effects. These processes can be shortened for drugs that serve a real specific illness or have been fastened by regulatory bodies for reaching a greater number of affected patients.

The number of computations and processes required to fulfill these requirements are extremely complex and have become a subject of interest even in the AI/machine learning domains. So where can quantum computing fit within the industry for dealing with clinical trials?

The Entire Process At Depth

Stage 4 trials are an important process of drug development and extend to the use of algorithms for determining the long-term effects of drug use. Powered by artificial intelligence (AI), these are coupled with in silico​ trials to help researchers to adapt quickly to a computer environment, transforming large data sets to interpret drug responses.

Machine learning has also been used conventionally for predicting earlier outcomes, and discovering new drug indications which can help experts avoid adverse reactions in specialized patients.

In silico clinical trials are often conducted through a series of pods consisting of human targets that meet certain requirements in terms of health and previous drug use. However, quantum computing can radically shift this by taking humans out of the equation.

Even if the technology is very far from being mature for this specific purpose today, quantum computing could assist in the development of virtual patients that react and interact with drugs the same way a normal human trial would. This would allow for clinical trial designs with as many virtual patients as possible, such that the components are chosen by the clinical trial sponsors.

Application At a Glance

Quantum computing could potentially reduce the time of clinical trials, the number of sites, and “real” patients, while also increasing their quality and veracity for future tests. Larger pharmaceutical giants have already initiated methods to create digital twins related to existing human populations which can also include population types that are most often not included in clinical trials.

  1. Pharmaceutical firms like Menten AI, Hafnium Labs, Kuano Labs, Riverlane, and XtalPi have created specialized in-house applications that are designed to find and create tiny molecules and macromolecules that might help cure ailments and disorders.

  2. Pharma is a natural space for quantum computing because it concentrates on molecular formulations and their interactions with larger populations on a longer time scale. These molecules (including those that may be utilized for synthesizing medicines) can be regarded as quantum systems or systems based on quantum physics, warranting a need for quantum computing formulations.

  3. Quantum Computing when applied to processing clinical trial studies and applying them towards modular predictions can be more successful than traditional computers at predicting and simulating the structure, characteristics, and behavior (or reactivity) of these drug molecules.

  4. The scope of converting clinical trials into behavior in a digitized format is still transitory and requires greater efforts from regulatory bodies, trial-based institutions, and research centers to work collaboratively with quantum computing think tanks.

Conclusions and Future

The landscape for using clinical data to better design and edit pharmacological compounds for their faster release and adoption is highly tied to how well interactions can be designed and predicted for the wider group of patients found in the market.

Quantum computing can become a champion for removing several of the trial complications and adverse reactions by virtually eliminating the need for humans. Getting to the next step of developing such algorithms requires more robust data collection and validation towards a single center that can be employed by quantum computing engineers everywhere.

Tune in next time as we explore other applications for quantum computing in pharmacological studies, drug design, and biotechnology.

Author - Amardeep Singh

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