Quantum Tools Amp; Industrial Supply Ltd
Hilda Bagnoli
In a recent article, we analyzed the impact of QC on the chemical industry, which, similarly to pharma, relies on the development and manufacture of molecules, and concluded that it will be one of the first industries to benefit. In this article, we explain the profound impact that QC could have on the pharma industry and present use cases for its application. We also provide a set of strategic questions to get clarity on the path forward for industry players.
Identifying and developing small molecules and macromolecules that might help cure illnesses and diseases is the core activity of pharmaceutical companies. Given its focus on molecular formations, pharma as an industry is a natural candidate for quantum computing. The molecules (including those that might be used for drugs) are actually quantum systems; that is, systems that are based on quantum physics. QC is expected to be able to predict and simulate the structure, properties, and behavior (or reactivity) of these molecules more effectively than conventional computing can. Exact methods are computationally intractable for standard computers, and approximate methods are often not sufficiently accurate when interactions on the atomic level are critical, as is the case for many compounds. Theoretically, quantum computers have the capacity to efficiently simulate the complete problem, including interactions on the atomic level. As these quantum computers become more powerful, tremendous value will be at stake.
The implications of these effects for QC are dramatic. Qubits can process far more information than conventional computers can. Qubits use the characteristics of quantum-mechanical systems to solve complex equations in a probabilistic manner, so a computation solved with a quantum algorithm enables sampling from the probabilistic distribution of being correct. The combination of greater speed with probabilistic solutions means that quantum computing fits well with a certain subset of computing needs and applications, such as optimization, the simulation of chemicals, and AI.
QC could make current CADD tools more effective by helping to predict molecular properties with high accuracy. That can affect the development process in several ways, such as modeling how proteins fold and how drug candidates interact with biologically relevant proteins. Here, QC may allow researchers to screen computational libraries against multiple possible structures of the target in parallel. Current approaches usually restrict the structural flexibility of the target molecule due to a lack of computational power and a limited amount of time. These restrictions may reduce the chances of identifying the best drug candidates.
In the longer term, QC may improve generation and validation of hypotheses by using machine-learning (ML) algorithms to uncover new structure-property relationships. Once it has reached sufficient maturity, QC technology may be able to create new types of drug-candidate libraries that are no longer restricted to small molecules but also include peptides and antibodies. It could also enable a more automated approach to drug discovery, in which a large structural library of biologically relevant targets is automatically screened against drug-like molecules via high-throughput approaches.
The following QC use cases apply to different aspects of drug discovery and will emerge at different points over an extended timeline. All of them, however, may enable more accurate and efficient development of targeted compounds.
Clinical trials could be optimized through patient identification and stratification and population pharmacogenetic modeling.3Paul et al., 2010. In trial planning and execution, QC could optimize the selection of the trial sites. QC could also augment causality analyses for side effects to improve active safety surveillance.
While the potential value of QC in pharma R&D is immense, it will also likely play a role further down the value chain. In the production of active ingredients, QC may aid in the calculation of reaction rates, optimize catalytic processes, and, ultimately, create significant efficiencies in the development of new product formulations. In the business-related value pools, QC in pharma could include the optimization of logistics (for instance, the optimization of on-site flows of materials, heat, and waste in production facilities) and improvements in the supply chain. Finally, toward market access and commercial, QC may even enable automatic drug recommendations.
The development of quantum computers began nearly four decades ago, but it is the gains in QC technology realized over the past few years that paved the way for practical applications in pharma. We see the key, value-adding QC activities in pharma unfolding over two distinct eras as the technology further matures (Exhibit 3):
In the next five to ten years, we expect that the first QC tools pharma players deploy will rely on hybrid methodologies that use classical algorithms alongside QC subroutines when they can create additional value. The prominent examples are the imaginary time evolution (an algorithm to find the ground-state and excited-state energy of many-particle systems) and the variational quantum eigen-solver, or VQE (an algorithm to calculate the binding affinity between an API and a target receptor). The value that algorithms such as VQE will add depends on the size of the quantum hardware. Describing small-molecule drugs generally requires less-mature quantum computers, while biologicals will be tackled only as QC matures.
Pharmaceutical companies should assess QC now and potentially lay the groundwork to reap the benefits of the technology later. QC may give many of them a huge opportunity, yet each pharma player needs to figure out how much exposure it has and the size of its QC opportunity in the context of its current pace of development. Thus, pharma players should consider three key strategic questions to determine their optimal QC strategy (Exhibit 5):
Some pharmaceutical players have already realized the need to join forces on the topic of QC and have started to collaborate and/or form partnerships. For example, QuPharm formed in late 2019 by major pharmaceutical players to pool ideas and expertise around QC use cases. QuPharm also collaborates with the Quantum Economic Development Consortium (QED-C), which was created in 2018 by the US government as part of the National Quantum Initiative Act and aims to enable commercial QC use-case efforts. Additionally, the Pistoia Alliance is a life sciences membership organization, which was organized to facilitate precompetitive collaboration and foster R&D innovation.
Digital talent gaps are already a reality, and QC may only exacerbate them. Unlike other important digital tools, such as AI, quantum computing depends on niche know-how. Pharma companies already struggle to attract people with capabilities in the less specialized digital technologies, and hiring quantum-computing experts may prove to be even more of a challenge.
The authors wish to thank Nicole Bellonzi, Matteo Biondi, Thomas Lehmann, Lorenzo Pautasso, Katarzyna Smietana, Matija Zesko, and the many industry/academia experts for their contributions to this article.
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