The impact of technology on clinical trial pipeline assets
By Alexandra Urman, Analyst
As proposed in our previous research, investment analysts using outdated debit rates to determine futures could undervalue clinical assets at every stage of development. Next Generation Sequencing (NGS), Artificial Intelligence (AI), CRISPR and other gene therapies should increase the efficiency of clinical trials, shorten development times and reduce failure rates. As a result, the return on investment could improve significantly.
Recent advances in protein modeling of neural networks could reduce clinical pipeline costs and increase drug R&D returns beyond what we initially modeled. Based on our ongoing research in this area, we now believe that preclinical development could become even cheaper and deliver results faster: a marketable therapy costs only $50 million in preclinical investment, half the number that we had previously estimated.
Alphabet subsidiary DeepMind has announced AlphaFold, a neural network-based algorithm that can predict protein folding, as well as an open source database of protein structures powered by AlphaFold2. Based on their amino acid sequences, the database predicts the 3D structure of almost all known human proteins, around 350,000.
Essential to life on earth, proteins consist of thousands of linked amino acids that determine their shape and their interaction with other molecules, including drugs. To understand these relationships, drug developers typically use X-ray crystallography – a common experimental method that is tedious, slow and expensive. Over the past few decades, scientists have used computational biology to overcome these hurdles, but none of the algorithms have proven to be accurate enough.
According to our research, innovative algorithms based on neural networks can accurately predict protein structures in laboratory experiments and could boost human therapies and agricultural breakthroughs. DeepMind’s AlphaFold2 has predictive accuracy that meets and sometimes exceeds high-fidelity experimental approaches and could apply to all proteins and protein-protein interactions.
Neural network-based algorithms could reduce preclinical trials Costs and boosting drug candidate pipelines
Knowing the shape of a protein structure early in research could cut the cost and time from discovery to approval of an Investigational New Drug (IND) by 50%. An AlphaFold-like algorithm that quickly determines the shape of proteins could improve economics more than our previous model suggested. For example, using throughput rates from past clinical trials (“Status Quo” in the table below), we estimated preclinical costs per marketed drug at $243 million. If technologies such as NGS and AI were to improve throughput rates, however, preclinical costs per marketed drug could be more than halved to $103 million (“NGS and AI” in the table below), and new predictive neural network AlphaFold-based algorithms could halve those costs at $52 million.
Assumptions: 15% discount rate in all phases except registration, which has a 3% discount rate, estimated peak sales of $1 billion for non-curative therapies, net margin of 70 %, 20% tax. Forecasts are inherently limited and unreliable.
Source: ARK Investment Management LLC, 2022
Our research suggests that neural network-based algorithms could reduce R&D costs, increase the value of clinical trial assets, and increase the likelihood of clinical trial phase transition. For example, enabling technologies could shorten the average time spent on preclinical experimentation. Historically, the duration of preclinical testing, from discovery to phase 1, has averaged four years. Based on our model assumptions, this number drops to three years and, with neural network-based algorithms, to two years, as shown below. Across the entire pipeline – from discovery to registration of a therapeutic drug – the technology could improve efficiency, reduce costs and eliminate 3.5 years of research and development time.
Assumptions: 15% discount rate in all phases, except for registration, which has a 3% discount rate; Maximum sales estimated at $1 billion for non-curative therapies; 70% net margin; 20% tax. Forecasts are inherently limited and unreliable.
Source: ARK Investment Management LLC, 2022
In our opinion, AlphaFold or similar technologies could increase the net present value (NPV) of a discovery asset from $30 million to approximately $170 million, as shown below. The old drug developers might have spent $120,000 and a year of experimentation to figure out a new protein structure. Today, this information is accessible in databases, and globally, which should increase the pace and success rate of experimentation at an early stage, implying both that the net present value of an early stage asset should be higher and that more of these assets will enter the pipeline. In other words, the cost savings are unlikely to reduce overall R&D expenditures for early-stage experimentation, but rather should increase the number of drug candidates entering the pipeline.
Assumptions: 15% discount rate in all phases, except for registration, which has a 3% discount rate; Maximum sales estimated at $1 billion for non-curative therapies; 70% net margin, 20% taxes. Forecasts are inherently limited and unreliable.
Source: ARK Investment Management LLC, 2022
The mass adoption and scaling of NGS, AI, neural network-based algorithms, and healing therapies could narrow the gap between the net present values of preclinical and clinical assets. In other words, we think the current early-stage pipelines, which growth stock investors often overlook, will be far more valuable than many realize.
- 1 https://alphafold.ebi.ac.uk/entry/P02088?utm_campaign=Sunday%20Newsletter&utm_medium=email&_hsmi=145809075&_hsenc=p2ANqtz-_tEuJqOJK3tW6xxkC-F2eOQDg3OzSPywVI1AfEeCacnTxM__gADzGG1tqjI5XGGXKxRa29buLtloed6YaKWUf0fssN-w&utm_content=145809075&utm_source=hs_email
The 3D structure of hemoglobin (a well-known protein in red blood cells that transports oxygen through the body) is characterized in the AlphaFold2 database, as shown in the link above.
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