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50 of the best-funded biotechs of 2023

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As the year draws to a close, it is clear that molecular science and diagnostics is the hottest funding area in the biotech industry. In an analysis of 50 of the best-funded biotechs of 2023 focused on human health, molecular and science and diagnostics startups collectively attracting roughly $945 million, dwarfing the figures in other segments. The next popular two niches, gene therapies and oncology, had average funding levels of approximately $245 million and $170 million, respectively. While AI has received a significant amount of attention this year, biotechs specializing in that field garnered an average funding of only about $66 million. Outside of the life sciences, startups with a broader focus on AI raised a cumulative average of $202.47 million, based on an analysis of close to 1000 companies.

Caris Life Sciences has raised nearly $1.7B to date

In terms of best-funded companies overall,…

Wearable developer Empatica aims to develop new digital biomarkers

Empatica Chief Medical Officer Dr. Marisa Cruz discusses advances in wearable technology and how new digital biomarkers could advance medtech.

Empatica Chief Medical Officer Dr. Marisa Cruz [Photo courtesy of Empatica]

Dr. Marisa Cruz envisions a future where unobtrusive wearable devices with advanced sensors will continuously measure and record actionable biodata without patients having to lift a finger.

Cruz is an endocrinologist and internist who serves as chief medical officer at Empatica, which develops wearable devices for monitoring patient physiology with the ultimate goal of improving clinical outcomes.

In 2011, Empatica spun out of an MIT lab focused on wearable sensors for continuous, passive patient monitoring. That technology is made possible by ever-shrinking sensors and batteries, gains in effective computing, and materials and manufacturing methods that result in comfortable and intuitive…

How Lantern Pharma and Code Ocean partnered on oncology drug development

A vision for data-driven drug development in oncology

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When Peter Carr, principal software architect of Lantern Pharma, stepped into his full-time role in September 2020, the company was on the cusp of a transformation. While AI had been a focus for a number of years, a fresh infusion of cash provided a possibility of expanding its AI capabilities and machine learning capabilities to drive down the cost of drug development in oncology.

Founded in 2012, the company went public in June 2020, raising $26 million. By the time he officially joined, Carr was already familiar with its operations, having previously worked as a consultant in 2019 to help set up the infrastructure. Carr joined full-time to help the company “expand their use of AI and machine learning for target discovery and patient stratification,” he recalled.

The challenge: Siloed research

While the company had experience in using A…

9 tips for implementing AI in medical devices from a Medtronic executive

Patients and healthcare providers remain at the core of successful AI implementations in medtech. [piai/Adobe Stock]

It seems like artificial intelligence (AI) is ubiquitous in the healthcare landscape, but the technology remains nascent in the industry. Technologies ranging from machine learning to natural language processing and beyond promise to help make diagnoses and treatment more precise, efficient, and personalized.

But the allure of AI can sometimes overshadow the central goal of addressing tangible clinical problems.

During his talk at DeviceTalks West, Ha Hong, chief AI officer at Medtronic Endoscopy, underscored the importance of putting patients and healthcare providers at the forefront when incorporating AI into medical devices.

With a plethora of AI tools at our disposal — many of which are increasingly user-friendly — the onus is on us to wield them responsibly. Below, you’ll find …

eClinical Solutions Q&A: The quest to transform raw data into drug discovery gold

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Top pharmaceutical companies sponsor over a hundred clinical trials annually, generating vast amounts of data. Harnessing this deluge is a monumental task. eClinical Solutions, led by CEO Raj Indupuri, tackles this through advanced applications of data analytics and machine learning with a strong emphasis on AI in clinical trials optimization.

Specifically, eClinical Solutions taps AI/ML for automated data mapping, classification, review and mining insights. This enhances efficiency, speeds up cycle times and ensures quality as data complexity grows. The company’s Elluminate platform integrates and structures data, supporting advanced analytics.

Powerful techniques like anomaly detection algorithms can automatically flag potential data issues for human review. ML also classifies and categorizes data to focus reviewer time on the most critical areas. Fostering an innovative cultur…

Investments in AI and ML help PV teams transform safety case processing

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Thanks to automation in pharmacovigilance, the next generation of safety is here — and with it, there is an immense opportunity for firms that can change how they work and maximize the opportunity automation creates.

Leading safety teams are seeing up to 80% efficiency gains on key workflows by investing in touchless case processing, automating pharmacovigilance processes across intake and assessment, review, full data entry, medical review, quality review and submission.

A new opportunity in AI and ML

The life sciences industry is rapidly growing in scale and complexity, with organizations generating increasingly large volumes of safety data. The pharmaceutical industry, healthcare providers and consumers have reported more than 18.6 million adverse events to the U.S. Food and Drug Administration (FDA) in the last 10 years, 216% more than the prior 10 years.

Safety case da…

Machine learning model flags patients with high risk of surgical complications

Improving the health of high-risk patients before their surgeries can lower mortality rates and cut healthcare costs. [Image by Gorodenkoff via Adobe Stock]

A newly developed machine learning model for surgical patients is automatically flagging those at high risk of complications to improve their odds of survival and reduce healthcare system costs.

Each day, the software reviews electronic medical records for patients scheduled for surgery and identifies those who might benefit from individualized coordinated care or prehabilitation to improve surgical results.

Researchers and physicians at the University of Pittsburgh and University of Pittsburgh Medical Center (UPMC) trained their algorithm on medical records for more than 1.2 million surgical patients. To help predict whether patients might suffer from complications after surgery, they focused the model on deaths from strokes, heart attacks and other…

Quantum computing promises new frontier in drug discovery and bioinformatics

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Quantum computing — described by pop astrophysicist Neil deGrasse Tyson as “computing with atoms” — is an emerging technology with a potential for immense computational speed and power. For some problems, quantum computers can be exponentially faster than classical computers, while for others the speedup may be more measured. The promise for drug discovery could be significant.

But the promises of quantum computing extend beyond mere increases in data processing horsepower. In an era dominated by generative AI models, which rely heavily on massive volumes of data for predictions, a different perspective emerges. Kristin M. Gilkes, EY Global Innovation Quantum Leader, underscores this shift in perspective. “I don’t believe we’ll need as much data with quantum computing,” she said. Gilkes sees the focus is shifting towards becoming data-centric, prioritizing the right set…

Why AI alone won’t resolve drug discovery challenges

A digital representation of a protein structure. [Image courtesy of NIH]

Big Pharma and researchers are sharpening their focus on AI to speed drug discovery. But the path to fully AI-driven drug discovery faces substantial hurdles, according to Adityo Prakash, CEO of Verseon. “When it comes to drug discovery, AI has a data problem with which the pharmaceutical industry has not yet come to terms,” he said.

Prakash explained there is simply “not enough data” to rely on AI as the primary means of small molecule drug discovery. He discussed these challenges further in an article in American Pharmaceutical Review titled “Exploring New Chemical Space for the Treatments of Tomorrow.”

The limitations and challenges of AI-first methods

Even with high-throughput screening to automate the process of testing pre-synthesized drug candidates against disease-associated target proteins, the pharma…

The Allen Institute is employing AWS Cloud and machine learning to decode brain mysteries

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High-resolution mapping of the human brain involves managing and interpreting a colossal amount of data. Shoaib Mufti, the head of data and technology at the Allen Institute for Brain Science, described the organization’s approach to these challenges in a recent interview. The project uses artificial intelligence to analyze millions of data points from brain imaging and genetic data, akin to the Human Genome Project.

Allen Institute’s AI-driven brain decoding

Mufti’s team at the Allen Institute is aiming to map the human brain at a cellular level. Their work has led to the creation of a genome-scale Allen Mouse Brain Atlas, a comprehensive, freely accessible online resource for neuroscience research, housing over 20,000 genes and more than 650,000 images.

The Allen Institute uses several AWS Cloud services to manage and analyze the vast amounts of data. They use Amaz…

Contrastive learning-based model ConPLEx elevates drug-protein interaction predictions

[Generative AI image from Tahsin/Adobe Stock]

Drug discovery, traditionally a labor-intensive process, often involves extensive computational work during experimental screening. Advances in AI, however, promise to streamline this process. To that end, a team from MIT and Tufts has introduced ConPLex, a computational model that uses large language model techniques, similar to those behind ChatGPT. The model analyzes vast amounts of text data to discern patterns and relationships among amino acids. The technique matches potential drug molecules to their target proteins without requiring complex molecular structure computation. The system’s efficiency allows it to sift through an array of more than 100 million compounds in a single day.

Bonnie Berger, head of the Computation and Biology group in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), and one of the senior authors of the new study, ex…

iBio’s chief reveals strategy behind AI-driven bispecific antibody discovery plans

Immunotherapy firm iBio (NYSEA:IBIO) has incorporated EngageTx, a machine learning-driven technology, into its development roadmap. This T-cell engaging antibody panel assists in generating bispecific antibodies targeting cancer cells. In particular, the firm is developing a novel Trophoblast Cell Surface Antigen 2 (TROP-2) bispecific molecule to target TROP-2-positive cancers.

A look at the rise of AI in oncology

As part of a broader trend, drug developers are employing machine learning in biotech to handle complex targets in areas like oncology, genomics, personalized medicine and rare diseases. In 2022, AI-employed companies had more than 150 small-molecule drugs in discovery and more than 15 in clinical trials, according to Boston Consulting Group. The group projected an almost 40% annual growth rate for the AI-fueled pipeline.

In a significant pivot, Bryan, Texas–headquartered iBio announced in November 2022 that it was divesting its contract development a…