First Human Trials Begin for AI-Designed Drug

First Human Trials Begin for AI-Designed Drug

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Forget college essays. Artificial intelligence has much bigger fish to fry.

Biotech firm Insilico Medicine said Monday that it entered an "AI-discovered-and-designed" drug into Phase 2 clinical trials involving human subjects, a first for the industry. The robots: they may not be so bad after all.

(Artificially) Intelligent Design

AI optimists have long pointed to advances in drug development as a reason for bullishness, and it's easy to understand why: The sheer data-crunching and protein-identifying prowess of such systems could potentially cut development time in half, and development prices by even more, proponents often claim. In plain English: AI can complete complex math problems far faster than human scientists ever could. Thus, AI and ML tools could help develop 50 new drugs worth potentially $50 billion over the next decade, according to a Morgan Stanley report.  ... ' 

How Artificial Intelligence is Matching Drugs to Patients

Considerable direction, following.  

How artificial intelligence is matching drugs to patients  in the BBC

Published,2 days ago, By Natalie Lisbona, Business reporter, Tel Aviv

Dr Talia Cohen Solal sits down at a microscope to look closely at human brain cells grown in a petri dish.   "The brain is very subtle, complex and beautiful," she says.

A neuroscientist, Dr Cohen Solal is the co-founder and chief executive of Israeli health-tech firm Genetika+.  Established in 2018, the company says its technology can best match antidepressants to patients, to avoid unwanted side effects, and make sure that the prescribed drug works as well as possible.

"We can characterise the right medication for each patient the first time," adds Dr Cohen Solal.

Genetika+ does this by combining the latest in stem cell technology - the growing of specific human cells - with artificial intelligence (AI) software.  From a patient's blood sample its technicians can generate brain cells. These are then exposed to several antidepressants, and recorded for cellular changes called "biomarkers".

This information, taken with a patient's medical history and genetic data, is then processed by an AI system to determine the best drug for a doctor to prescribe and the dosage.

Although the technology is currently still in the development stage, Tel Aviv-based Genetika+ intends to launch commercially next year.  .... '

Researchers Develop Tool to Identify Existing Drugs to Use in Future Outbreak

More Sim advances link existing drugs to future outbreaks.

Researchers Develop Tool to Identify Existing Drugs to Use in Future Outbreak

By New York University, March 9, 2023

“Drug repurposing strategies provide an attractive and effective approach for quickly targeting potential new interventions,” said Bud Mishra, a professor at NYU’s Courant Institute of Mathematical Sciences.

An artificial intelligence algorithm developed by a global team led by researchers at New York University (NYU) can identify existing drugs that could be repurposed during future pandemics.

The PHENotype SIMulator (PHENSIM) simulates tissue-specific infection of SARS-CoV-2 host cells and calculates the antiviral effects of existing drugs via in silico experiments that take into consideration selected cells, cell lines, and tissues under various alterations of biomolecules.

The tool's effectiveness in identifying drugs for repurposing was confirmed by comparing its results with recent in vitro studies.

NYU's Naomi Maria said, "We've been able to model the SARS-CoV-2 infection and identify several COVID-19 drugs currently available as potentially effective in battling the next outbreak."

Added NYU's Bud Mishra, "Identifying and selecting ahead of time the best candidates, prior to costly and laborious in vitro and in vivo experiments and ensuing clinical trials, could significantly improve disease-specific drug development."

From New York University 

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How Generative AI Could Lower Healthcare Costs, Speed Up Drug Development

 Interesting App in this space, makes much sense. Looking forward to prices going down.

How Generative AI Could Lower Healthcare Costs, Speed Up Drug Development

By ZDNet, March 3, 2023

Absci executives are confident generative AI programs will lead to antibodies with high efficacy in binding to disease targets.

When OpenAI's GPT-3 natural language processing software burst on the scene in 2020, one of the most remarkable things about it was its ability to carry out a variety of tasks in a "zero-shot" fashion. Zero-shot fashion means without having been given any explicit examples of the task, such as printing the French word "rate" when a person types the phrase "translate the word spleen into French," despite never being trained explicitly to translate.

In the near future, AI programs may be able to develop new cancer drugs in a zero-shot fashion, inventing combinations of amino acids that bind to cancer cells and neutralize them with no prior example of an effective protein.

From ZDNet

Computer Models Determine Drug Candidate's Ability to Bind to Proteins

Pharma applications? 

 Computer Models Determine Drug Candidate's Ability to Bind to Proteins   By University of Arkansas, January 17, 2023

The research focuses on computational simulations of diseases, including coronavirus.

University of Arkansas (U of A) researchers have developed computer models for calculating a drug candidate's protein-binding affinity.

Said U of A's Mahmoud Moradi, the method "assigns an effective energy to the ligand at every grid point in a coordinate system, which has its origin at the most likely location of the ligand when it is in its bound state."

The researchers produced a computationally efficient binding estimator using biased simulations and non-parametric re-weighting techniques, then applied orientation quaternion formalism to further define the ligand's conformational shifts while binding to targeted proteins.

They used the method to estimate binding affinity between human fibroblast growth factor 1 and heparin hexasaccharide 5 medication.

From University of Arkansas   

AI Helps Generate Building Blocks for New Drugs

Generating New Drugs

AI Helps Biotech Labs Generate the Building Blocks for New Drugs

By Adrianna Nine on December 6, 2022 at 7:56 am Comments

Myotis daubentonii. (Credit: Generate Biomedicines)

Proteins are an essential part of life. Not only do they function as the “building blocks” for living organisms, but they also perform nearly every cellular task, from waste management to tissue repair. It tracks, then, that pharmaceuticals often contain or “target” proteins in an attempt to change or eliminate symptoms or disease within the body. There’s just one little problem: The only proteins we can use to create drugs are the ones we know.

But what if we could add new proteins to the resource pile? Two biotech labs have begun using artificial intelligence (AI) to generate novel proteins with the goal of incorporating them into medicine. Chroma, a program by Boston-based Generate Biomedicines, and RoseTTAFold Diffusion, a program out of the University of Washington’s Baker Lab, are often compared with trendy AI art generators thanks to their ability to create new concepts seemingly out of thin air. In fact, Chroma has even been referred to as “the DALL-E 2 of biology.”

AI protein generators, a type of denoising diffusion probabilistic model (DDPM), are trained to pull samples from complex datasets. They then use those samples—plus added noise—to construct a product that matches a given prompt. Although art generators are the best-known DDPMs, AI isn’t only good for producing images on demand; they’ve also been found to be an ideal method of generating diverse protein models. In a preprint on bioRxiv, Baker Labs scientists describe how RoseTTAFold can devise protein structures with virtually any desired size, shape, or function, as well as with any required constraints. ... ' 

AI For Vaccine Rollout and Tracking

In particular for tracking deployment in complex supply chains with complex data. 

How AI is Helping with COVID-19 Vaccine Rollout and Tracking 

December 17 

Covid-19 Coronavirus Vaccine vials in a row macro close up in AITrends  By John P. Desmond, AI Trends Editor  

AI has been employed since the early days of the COVID-19 pandemic to track the spread of positive cases, to crunch through thousands of scientific papers to search for treatment options and to help develop a vaccine. Now AI and other digital tools are being deployed to manage complex supply chains for the vaccine. 

With the third highest number of coronavirus cases in Europe after France and Italy, according to data from Johns Hopkins University, the UK is the first country to distribute the Pfizer vaccine. The UK has 1.7 million confirmed cases in a population of close to 68 million people, Tracking side effects from the vaccine rollout is a huge task, UK health officials have said, according to an account from Nasdaq. To help meet the challenge, the UK Medicines & Healthcare products Regulatory Agency (MHRA) recently partnered with the UK unit of Genpact, the global professional services firm specializing in digital transformation. The company is integrating components of its AI software suite with the government’s website where adverse effects are reported. 

“When a vaccine gets distributed at scale and speed, a technology solution needs to track the batch and lot numbers to know exactly where each dose is and who received it,” stated Eric Sandor, drug safety AI lead at Genpact. “There’s a lot of information, in a number of different formats, and it’s very manually intensive to try to codify it in a way that makes sense. AI will help with processing all that data faster than humans can. It’s quite complicated at scale, but is a critical element to overall public health.”    ... ' 

Computational Models Help Translate Drugs From Animal Studies

Could be a considerable development.    We need better drug development methods now.  And expect considerable pushback here too.

Computational Model Could Improve Success in Translating Drugs From Animal Studies to Humans
Purdue University News
By Kayla Wiles

Researchers from Purdue University and the Massachusetts Institute of Technology created a computational model to help translate drug development from animal studies to people. The TransComp-R model consolidates thousands of measurements from an animal model to a small number of data coordinates for human comparison, highlighting the most relevant sources of biological divergence; scientists could then train other models to anticipate human response to treatment. The researchers used TransComp-R to find an overlooked biological mechanism that may underpin human resistance to the inflammatory bowel disease drug infliximab. Purdue's Doug Brubaker said, "The modeling framework itself can be repurposed to different kinds of animals, different disease areas, and different questions. Figuring out when what we see in animals doesn't track with what's happening in humans could save a lot of time, cost, and effort in the drug development process overall."

Drug Delivery

Once again,  its the accurate and efficient delivery problem.  Often a key component of process.

COMMANDing Drug Delivery
MIT News
Sabbi Lall
July 10, 2020

Massachusetts Institute of Technology researchers are using a computational approach to help deliver drugs that treat brain disorders more effectively. The new COMMAND (computational mapping algorithms for neural drug delivery) approach takes into account the irregular shape of the target brain region, which could facilitate a more specific form of drug delivery using a single catheter. The system aims to maximize on-target and minimize off-target drug delivery. Using computational simulations, the researchers were able to deliver drugs to both compact brain structures and to broader, more irregular regions. Said MIT's Ashvin Bashyam, "COMMAND applies a simple principle when determining where to place the drug: Maximize the amount of drug falling within the target brain structure and minimize tissues exposed beyond the target region."   ... '