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UPCOMING EVENTS
July 22: NJx Pig Roast | Lambertville
July 23: Fourth Effect: Board Boot Camp | online
AI Adoption: From Naïve to Native | online
TiE New Member Welcome Happy Hour | Bridgewater
July 25: NJ Code & Coffee - July meetup
July 29: Portal Pours at the New Jersey Innovation Hub | New Brunswick
Here’s a reminder that the JT+I website offers many resources for entrepreneurs and innovators; there is also a LinkedIn page, so please follow.
Entrepreneurial Support
Government/EDA/SIC Hubs
Capital Resources
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Hope to see you soon!
Jim Barrood and the Jersey Tech + Innovation Team
Good pieces, courtesy of AXIOS:
How AI is supercharging drug development
Artificial intelligence is dramatically speeding up drug development — and a proprietary survey shows how it's priming a bigger automation push that's changing the trajectory of early stage pharma research.
Why it matters: The TD Cowen survey of 80 biopharma leaders and insiders finds AI is compressing drug developers' preclinical costs and timelines by as much as 70%.
That's fueling demand for cutting-edge software, sequencing tools and computer models that will help churn out more experimental treatments in the next five years.
The big picture: AI can't replace scientific intuition. But the way it can streamline the grueling R&D before human testing begins is making computer screens as important a feature of drug design as traditional laboratories.
The hope is to "create more shots on goal," says Brendan Smith, director of life sciences equity research at TD Cowen, and to generate large amounts of data that can train the AI models to increase the odds of clinical success.
There's a considerable downstream effect on "wet labs," where scientists will still evaluate the safety and effectiveness of the compounds.
Between the lines: The idea of a continuous research loop contrasts with the AI-fueled disruption and job losses hitting parts of white-collar America — though some segments of pharma could still be affected.
AI is fixing problems we couldn’t solve before - here’s the proof
This video is brought to you by 80,000 Hours. Hi, welcome to another episode of Cold Fusion.
The Trump administration's push to reduce animal testing in biomedical research will probably pivot more work to computational tools, 3D human tissue models and other alternatives that predict the toxicity of a compound.
Demand for advanced software that can simulate biological processes and predict how two drugs can interfere with each other or adjust dosages for newborns and pregnant women will see the strongest upside by 2028, the survey finds.
Companies already are pouring money into prediction and modeling tools like "in silico" platforms that allow scientists to run thousands of virtual experiments in seconds and simulate the toxicity or stability of a drug.
By the numbers: The proprietary survey data indicates new drug development programs could grow by more than 10% in three to five years.
That technology buying spree, along with more spending on labs, could account for an additional $1 billion in incremental spending.
Reality check: AI hasn't yet discovered a drug that's won Food and Drug Administration approval. And some investors question its ability to have a significant impact on patients.
One concern is that all the engineering and optimization may not sufficiently factor in human responses — and how different people are — before the compounds reach clinical trials. Without more of that, skeptics say the new drug failure rate could remain around 90%.
What we're watching: China's biotech buildup continues to threaten U.S. research efforts by offering cheaper labor and quick turnaround times that already are attracting billions in new investment.
The administration "keeps moving the goal post" on AI policy, trying to straddle a hands-off regulatory approach with more oversight of safety and privacy concerns, TD Cowen notes.
**Axios Finish Line: Lessons from the gray area
I'm turning 60 this year, and this milestone has me thinking about the value of aging beyond economic power or trendy hair. It's perspective.
The big picture: Like a lot of older Gen Xers, I have stories of buying an annual road atlas, talking for hours on a corded phone in the kitchen and witnessing the first Macintosh appear in my college computer lab.
I remember looking at that "mouse thing" and feeling quite sure it would never catch on.
Between the lines: I've been wrong about so many things. And if 60 has taught me anything, it's that being wrong isn't the worst thing. Refusing to learn is.
Here's some advice from someone who has more gray hair than answers — a few things I wish I'd known when I was younger.
Write it down. You think you'll remember everything, but you won't. You'll remember the big things, mostly. But the small things — the funny thing your kid said, the long days at the pool — those have a way of slipping away.
Your friends — your support system — are everything. In a family emergency, they were the ones who organized meals, picked up my daughter from college and showed up when I couldn't ask.
Make the connection. In my 30s, a retired couple lived across the street from us. They were the neighbors who went out of their way to do kind things, like installing a tree swing in their yard for the neighborhood kids to use.
Then life got busy. We rushed through school years, work deadlines, practices, dinners, all of it. Somewhere along the way, I lost that connection, and I will always regret that.
The bottom line: Maybe graying means you've lived long enough to be wrong about a few things — and lucky enough to keep learning.
Courtesy of Jim VandeHei, CEO, Axios
