A funding drought, plummeting valuations, and the recent shutdown of Ketamine Wellness Centers — one of the largest chains of ketamine clinics — signal that investors may have soured on the space.

In this brief, we use CB Insights data to assess the current state of the psychedelics market. We look at the following data points:

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Knowledge discovery products use machine learning and semantic search algorithms to surface relevant scientific and experimental data. Companies in this space offer solutions that take a variety of public or private data sources as input.

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Cloud research and development (R&D) allows for digitizing drug discovery and preclinical and other R&D laboratory workflows and processes. Typical features include electronic lab notebooks and lab information management systems.

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Nature mining uses biological data from natural sources — such as fungi, microbes, and plants — to discover novel drug candidates. Platforms typically collect data through genome sequencing and/or manually cataloging molecules produced by the organism. Then, they apply machine learning (ML) to parse the data and uncover drug candidates.

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This presents a massive opportunity for big tech companies. Drug discovery technology is a $120B market, and corporate executives are beginning to notice.

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Over the last several years, Novartis has acquired, partnered with, and invested in companies that support its mission to leverage artificial intelligence (AI) and other technology to improve its processes and create new products.

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The space is undergoing a digital transformation driven by an explosion in healthcare data and use cases for AI. Incumbents are quickly learning that inexperienced data science teams can cause more harm than good.

In response, big tech companies are building tech to streamline drug discovery, clinical trials, manufacturing & supply chain, and remote patient monitoring & health records.

Some tech giants will likely succeed in healthcare — as partners, competitors, or both — and will play a key role in pharma’s digital transformation. In this report, we analyze the trends driving each big tech company’s pharma strategy, their current initiatives, and what it means for the industry.

Download the report to find out:

• Which pieces of the pharma value chain big tech is attacking
• How big tech’s product strategies differ across drug discovery, clinical trials, manufacturing & supply chain, and patient monitoring & health records
• How big tech companies leverage partnership deals with incumbents to expand their pharma offerings
• Which strengths and capabilities differentiate each tech giant from others in pharma

REPORT HIGHLIGHTS:

• Big tech companies are likely to double down on hardware and software, with the aim of capturing as much patient data as possible. The amount of data generated from these products is significant. Expect big tech companies to utilize their data expertise to discover and commercialize digital biomarkers.
• The number of pharma acquisitions by big tech companies dropped steeply in 2022, after a very active 2021. Deals to pharma startups with big tech participation also slid over the same period. Alphabet led big tech in unique pharma investments in 2022 by a wide margin, followed by Microsoft.
• Big tech companies will continue to offer AI expertise to pharmaceutical companies. Anticipate big tech to begin negotiating contracts with shared upside for drugs discovered as a result of their pharmaceutical partnerships.

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Digital biomarkers for vital signs allow providers to monitor patients in real time and enable pharmaceutical companies to achieve better trial endpoints. Typical variables include body temperature, pulse, respiratory rate, and blood pressure, all of which can help guide treatment decisions.

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Patient data & analytics companies provide anonymized patient genomic datasets and supporting analytical tools, such as genomic variant analysis software, to fuel drug target discovery and patient stratification. Many solutions also incorporate other data sources, such as electronic health records and transcriptomic and proteomic data.

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Digital therapeutics (DTx) for diabetes includes apps and software that leverage evidence-based behavior change strategies, such as cognitive behavioral therapy (CBT), connected glucose monitors, and smart scales, to help patients manage and improve their diet and lifestyle choices.

Diet and lifestyle are the primary drivers of diabetes diagnosis and progression. Combining medication with therapy to modify behaviors can further improve the progression of diabetes treatment.

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Artificial intelligence (AI)-powered drug discovery companies in the pharma market are analyzing data, such as clinical trial data, electronic health records, omics data, cellular images, and more, to discover novel drug candidates. Machine learning alone is no longer a differentiator. Startups are increasingly incorporating laboratory automation and experimentation to create high-quality biological data to train their algorithms.

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During the first year of the Covid-19 pandemic, global anxiety and depression rates increased by 25%. Meanwhile, 42% of Americans who reported needing mental healthcare over the past 12 months did not get it because of low availability and affordability.

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Unfortunately, the return on this investment has been declining for pharmaceutical companies over the past few years. It has become harder to discover efficacious drugs, and novel therapy modalities — such as gene therapy — have experienced setbacks in clinical trials.

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Key trends to watch in the space include:

• Digital therapeutics: These software-driven, evidence-based therapies are gaining traction as viable solutions to help manage mental health, diabetes, and more.
• Real-world data use cases: Alternative datasets — gathered from sources such as patient registries, EHRs, and insurance databases — are being used to supplement clinical trial outcomes, personalize treatment, and create “digital twins” of patients.
  
• Patient-specific therapy: Advances in genomic sequencing and molecular diagnostics are enabling pharmaceutical companies and providers to develop and deliver therapies with higher probabilities of success.

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The term remote patient monitoring (RPM) refers to the use of digital technologies to capture health data from patients and electronically transmit this information to healthcare providers.

RPM technologies, such as wearables, enable real-time data capture in a hybrid or virtual setting and minimize the need for in-person visits. This technology remotely and continuously collects patient-generated data, which enables greater access to care outside of a conventional setting, improves efficiency, and empowers patients to better manage their health.

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Digital twins and biosimulations use model-based simulations to enable partially or fully virtual clinical trials by simulating versions of living humans.

In this case, digital twins are virtual models of patients. Using a variety of data sources, such as electronic health record (EHR) and claims data, digital twins can estimate outcomes for patient populations in clinical trials. Digital twins integrate simulation tools that help pharmaceutical companies optimize the complete product lifecycle, from research to production.

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Real-world data (RWD) encompasses data relating to the health status of patients and the delivery of care. RWD can come from a variety of sources, such as patient registries, electronic health records (EHRs), insurance databases, social media, and patient research networks.

The use of computers, mobile devices, and biosensors is increasing in healthcare. Consequently, health-related data has rapidly increased, making it possible to plan and carry out clinical trials and research investigations more efficiently.

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Recruitment and enrollment software helps sponsors and sites connect with patients for clinical trials and track their enrollment in studies. These solutions structure the inclusion and exclusion criteria in clinical trial listings and match them against patient responses.

Some tools help match patients with clinical trials directly, while others provide drug developers or clinical trial operators with access to health data that can help find eligible patients.

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Pharma companies are applying emerging technology across the entire pharmaceutical value chain to save time and money.

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Remote patient monitoring (RPM) captures and analyzes patient data in real time to enable virtual care and ongoing disease management. The technology has emerged as a viable tool to improve the treatment and management of chronic conditions. Current solutions include wearables and self-diagnostic kits that track and monitor a patient’s vitals. 

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Startups creating new ways to measure digital biomarkers, enabling clinicians to monitor patients outside of the hospital and improve overall outcomes. Using data like blood pressure, eye movement, glucose, and brain activity, doctors can create unique patient treatment plans.

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The tech giant has made 90+ investments in the sector over the last 2 years. These investments complement activity from Google’s existing pharma-focused ventures, Verily Life Sciences, Calico Life Sciences, and Isomorphic Labs. For example, Verily recently raised $1B to further its mission to enable precision health.

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Clinical trial management software (CTMS) streamlines clinical operations by providing insight into research portfolios and ensuring regulatory and billing compliance. These solutions also centralize and standardize the billing process, including sponsor invoicing and subject visit tracking.

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