Drug Discovery and Development Archives - Page 5 of 159

2024: AI and scientists take turns at the wheel of drug discovery

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In drug discovery, interest in harnessing the power of AI ramped up significantly with breakthroughs like AlphaFold, where AI predicted protein structures with astounding accuracy. AI’s initial focus was analyzing existing data, with machine learning systems excelling at tasks like predicting new drug interactions, molecular behaviors, and even biological pathways, based on troves of experimental data. ML can also aid in identifying promising drug targets by using natural language processing to analyze scientific literature.

But AI’s role is rapidly evolving. In 2024, AI is poised to transition from analyzing existing data to a more proactive drug discovery role as a predictor and collaborator. Shifts fueling the trend include the rise of generative AI, which can create novel molecular structures and predict their properties. An…

Survey: Wielding AI magic in clinical trials requires a master’s touch

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eClinical’s Industry Outlook 2024 report highlights a significant acceleration in AI/ML adoption for clinical trials. Over half of professionals (53%) in functions like clinical operations, data management, and biometrics now see these technologies as central to streamlining trials by 2024, surpassing the emphasis on automation that dominated last year. Despite this hype cycle, a core lesson emerges: progress will be uneven as we learn to harness this new form of “magic.”

The sorcerer’s apprentice: Enthusiasm and the need for mastery

Much like the eager apprentice in Disney’s animated film “Fantasia,” which itself is inspired by Goethe’s “Der Zauberlehrling”(“Sorcerer’s Apprentice“), many in the clinical research field seem eager to unleash the power of AI even before fully grasping the strategic investment required for mastery…

Big Pharma clicks soared, but new cell therapies made you buzz: What drove biopharma interest in 2023?

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The big Big Pharma names you couldn’t ignore

Despite biopharma’s 2023 layoffs and challenges, innovation won your clicks last year with over 130,000 of them on our Pharma 50 report alone. But while giants dominated, your clicks showed disruptive tech wasn’t far behind. The next-most popular article was a roundup of 100 trailblazing cell and gene therapy companies with more than 80,000 views. Other hits included companies putting AI in drug discovery and development, and the ever-popular biotech startup watch – proof that solutions, not just statistics, attract attention. Rounding out the top five was a roundup of biotech job cuts and openings.

2023 biopharma innovation in the spotlight on Linkedin

But over on LinkedIn, innovation was also a driver of interest. Topics on women’s health, such as Organon doubling down on women’s health, novel approaches to mental illness at…

Skynet with benefits: Can AI and humans become a drug discovery superorganism?

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Will the credit for future mega-blockbuster drugs, in some cases, go to a carefully-programmed AI discovery system connected to a “self-driving lab” that verified its potential?

Certainly, AI is hyped, but so are potential profits of potentially AI-optimized drugs. The exploding volumes of scientific data highlight a shift often overlooked: what does “inventor” even mean when human brilliance relies on AI and vast datasets no single person can comprehend? This future depends in part on connecting the dots between data experts, lab scientists with domain knowledge, and the machine learning systems capable of pattern recognition humans can’t even fathom. But the crux isn’t simply generating more data, and making it a shared, dynamic force fueling breakthrough discoveries — a force deeply integrated with computation and human expertise.

Breaking through the data bottleneck

Gain Therapeutics’ supercomputer-driven therapy offers potential Parkinson’s breakthrough

The AI-powered drug discovery platform Magellan paved the way for GT-02287, a potential disease-modifying treatment for Parkinson’s disease. [Image: Gain Therapeutics]

The Bethesda biotech Gain Therapeutics, armed with supercomputer firepower, aims to disrupt Parkinson’s disease treatment. Their GCase regulator, named GT-02287, completely restored motor function in a recent animal study.

GCase, short for glucocerebrosidase, targets both lysosomal and mitochondrial dysfunction. In Parkinson’s, the failure of cellular powerhouses (mitochondria) and recycling centers (lysosomes) disrupts essential functions. This breakdown leads to a toxic buildup of glycosphingolipids (fatty substances within cells) and damaging clumps of alpha-synuclein (a misfolded protein), hallmarks of Parkinson’s disease and the cause of Lewy bodies. For a visual explanation of this process, see the diagram several par…

When will drug development have its ChatGPT moment? Inside ambitious AI initiatives at Sanofi and Medable

In episode 4 of Ai Meets Life Sci, Kayleen Brown, managing editor at DeviceTalks and Brian Buntz, pharma and biotech editor, chat with Helen Merianos, Ph.D., head of R+D portfolio strategy at Sanofi and Michelle Longmire, MD, CEO of Medable. The focus? The two-fold application of AI in their respective companies’ technologies, both for scientific advancement and business productivity, were central themes. Sanofi is applying AI across the company, encouraging an inquisitive culture around product development. AI also aids in making more data-driven investments across various domains. Medable is tapping AI to build a culture of invention as decentralized clinical trials become more operationalized and scalable.

Tune in and subscribe to AI Meets Life Sci on all major podcast channels and follow youtube.com/@DeviceTalks or AI Meets Life Sci YouTube Podcast to ensure you never miss an episode.

Sanofi embraces AI for improved decision-making

In a June 2023 press rele…

Beyond diabetes and obesity: Can GLP-1 therapies also transform chronic disease treatment?

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Glucagon-like peptide-1 (GLP-1) receptor agonists like semaglutide and tirzepatide have cemented their status as two of the most successful drugs in recent memory. Recent projections have estimated that the drug class could fetch $44 billion by 2030 and $71 billion by 2032.

But GLP-1 sales could potentially reach greater heights as these therapies move beyond their established territories of diabetes and obesity and start tackling major conditions like chronic liver and kidney disease, Alzheimer’s disease, and heart failure.

Promising clinical trial results highlight the potential of GLP-1s in an array of disease, including chronic kidney disease, nonalcoholic steatohepatitis (NASH), various forms of heart failure and potentially even Alzheimer’s.

There are, however, hurdles, such as a relatively high rate of gastrointestinal (GI) side effects, which affect somewhere around 40–7…

Unifying disparate data in clinical trial management with advanced data technology

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The increasing complexities in clinical trials, including expanded patient populations, decentralized trials and new technologies, are directly impacting the amount of data and information available to clinical trial sponsors. Though this provides significant value to the industry, it presents a new challenge of consolidating disparate and siloed systems, improving standardization and unifying the explosion of data across clinical trials.

Organizations are now faced with the need to centralize data and are increasingly adopting advanced data and analytics capabilities to bring efficiency to data centralization and democratization processes.

Regulatory challenges, incongruous data and increasing stakeholders

As accountability shifts with regulatory guidelines, sponsors increasingly carry the burden of clinical trial oversight, taking responsibility for any unforeseen proble…

Off with the training wheels: AI-based patient characterization can improve clinical trial performance without large data sets

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Only 12% of new drug candidates that enter phase 1 clinical development ultimately receive FDA approval. This dismal success rate leaves millions of patients with unmet medical needs and drives up the costs for the small number of drugs that make it to market. More frustratingly, it leaves untold numbers of potentially transformative therapies back-burnered or discarded entirely, not because they don’t actually provide benefit, but because they were tested in trials that weren’t effectively designed to demonstrate benefit. The true failure hasn’t been in drug innovation but in identifying the patient traits that govern clinical trial outcomes.

The big challenges of big data methodologies

Artificial intelligence (AI) holds great promise in improving this success rate by providing data-driven approaches to identifying traits and their combinations that enable more effective paradigms to enrich patien…

BIOiSIM assigns ‘credit scores’ to drug candidates

VeriSIM Life, a San Francisco-based startup, has created BIOiSIM, an AI-powered platform that simulates drug compound behavior in the human body by acting like a virtual laboratory. It assigns each a predictive “credit score” predicting its viability for drug development.

“It’s like a FICO score for drug development,” founder and CEO Dr. Jo Varshney said. BIOiSIM’s scoring system draws from an inferential search space of more than 1 billion drug-like compounds and capacity to model 800 billion total simulation scenarios powered by 10 billion deep learning neural network effects. The vast search space of drug-like compounds and data processing capabilities enable it to evaluate numerous factors such as its potency, selectivity, and safety profile to determine a drug’s overall likelihood of success.

BIOiSIM focuses on predictive insights

The considerable scale of data allows for a comprehensive analysis of drug compounds, bolstering the pr…

Bridging the gap: How resource sheets translate complex clinical trial data into patient empowerment

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As patients take a more active role in managing their health, they can find navigating the complexities of clinical trial results daunting. Having access to clear, accurate information is crucial for informed decision-making. This is where patient-reported data from clinical trials has real value, offering key insights into the real world experiences of participants, both positive and negative. To bridge the gap, patient-centered resource sheets, developed through collaborative efforts between researchers and patients, provide a valuable information source that translates clinical information into easy-to-understand language and is presented with sensitivity and empathy. More than just presenting data, these resources explain and contextualize it for individual patients, bridging the gap between research and their specific needs. The result? Patients feel confident enough to make informed choices that ult…

GLP-1 drugs could open a new frontier in NASH treatment

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This morning, Eli Lilly reported positive phase 2 results for its dual GLP-1 and GIP receptor agonist tirzepatide in patients with nonalcoholic steatohepatitis (NASH). In the SYNERGY-NASH trial, the therapy achieved NASH resolution without worsening fibrosis in 61.3% of patients. That is considerably higher than data for semaglutide.

Picturing tirzepatide’s NASH resolution in a phase 2 study

The bar graph below depicts the proportion of participants showing no worsening of liver fibrosis at 52 weeks with varying doses of tirzepatide compared to a placebo in the SYNERGY-NASH phase 2 study. See page 17 here for the data source.

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