All new medicines introduced in the market are the result of lengthy, costly, and risky R&D processes. Delivering a new medicine to market takes on average 10–15 years from the first synthesis of a new active substance to the final product, and the costs can be as much as €5B. Despite massive investment, only 1–2 in every 10,000 substances successfully pass all stages of development to become a marketable medicine.
Drug formulation is a crucial stage of drug development, estimated to cost approximately €3B a year to European pharmaceutical companies, and is a major cause for project failures and delays in the manufacturing process.
It has been estimated that 80–90% of all New Chemical Entities (NCEs) have poor solubility which severely limits their bioavailability and therapeutic efficacy. Many programs are suspended or slowed down before progressing into animal and human studies due to the lack of adequate tools supporting the industry to overcome these solubility and bioavailability challenges.
The industry therefore still depends on skilled formulation scientists to solve these challenges, who are increasingly hard to find and retain. Effective computational tools are urgently needed to increase labor efficiency.
Ionic liquids (ILs) are a promising new class of solvents that can empower the pharma industry by solving low solubility, stability and bioavailability of compounds.
ILs can be designed as eco-friendly, low-toxic and low-cost solvents with exceptional solvation and stability abilities and offers opportunities to dissolve drugs that are insoluble or poorly soluble in water and in the most used solvents, improving their therapeutic properties. These unique properties make ILs very attractive for pharma and several other industries.
Yet, with 10¹⁸ possible compositions, finding the perfect IL composition for an NCE requires countless experiments to determine the properties of the chemical system under research (including solvation capabilities), which surpasses the human and financial capabilities of the industry.
The pharmaceutical industry has started to adopt various computational methods to reduce the time and cost of drug discovery and development. But the industry lacks access to appropriate computational platforms capable of accurately predicting the best formulation for a drug candidate in solvent systems and especially fine-tuned to IL systems, which is key to making the drug formulation process fast and cost-efficient.
So far, no solution can quickly and accurately determine the chemical property data of drug-IL systems, limiting the sector’s capacity to quickly find the best-matching IL-based formulation and thereby take full advantage of these solvents. Existing classical software programs designed to predict thermodynamic properties of molecules, such as the solubility coefficient, often lack certain physical property data or models which make them not applicable to new molecular structures.
Some solubility models demonstrate lower than optimal accuracy (e.g., Hansen solubility model). Recent advances in ML have enabled automatic predictive modeling from past experimental data, but large datasets are needed, which are often biased, and the prediction models show poor performance when extrapolating outside of the training set of NCE.
Also, despite an increasing trend for digitalization in pharma, many companies find AI tools difficult to use due to the complex user interface and shortage of skilled personnel to utilize them. Therefore, the pharma industry needs new technologies allowing fast prediction of the best IL formulation for difficult-to-dissolve NCEs.
The solution lies in quantum chemistry since molecular systems and, therefore thermodynamic properties are quantum by nature. Easy-to-use quantum chemistry-based prediction tools can revolutionize drug formulation and boost the adoption of ILs in the industry, thereby contributing to reduced time and resources spent.
Solving the current challenges facing pharma companies in respect to drug formulation is a big, global opportunity. The drug formulation market, which was valued at €1.6T in 2022, is expected to increase at a CAGR of 6% from 2022 to 2032 surpassing €2.95T by the end of 2032.
The main market growth drivers are the introduction of novel technologies as well as more affordable and cost-effective methods. Robotics and AI are increasingly being used in this industry to reduce production waste and downtime on the factory floor. In parallel, quantum computing is advancing to surpass current technological limitations.
Other big growth drivers are the rising world population, the increased numbers of the elderly, and the growing prevalence of chronic diseases among the population.
Read also: Molecular Quantum Solutions (MQS) Investment
Joachim Schelde is a Senior Associate at Scale Capital, a Danish venture fund investing in Deeptech companies using AI and Quantum computing technologies.
Scale Capital a Danish venture fund investing in digitization and disruptive technologies within B2B. €1–3M in Nordic and German B2B tech startups at Seed and Series A, and helping them win in the US.
Scale is headquartered in Copenhagen with a presence in the Nordic countries and Silicon Valley.