His lab uses mice for some protocols, but animal studies are notoriously bad at identifying human treatments. Around 95% of the drugs developed through animal research fail in people. Researchers have documented this translation gap since at least 1962. "All these pharmaceutical companies know the animal models stink," says Don Ingber, founder of the Wyss Institute for Biologically Inspired Engineering at Harvard and a leading advocate for organs on chips. "The FDA knows they stink."

One evening in September of 1957, viewers across America could turn on their television sets and tune in to a CBS broadcast during which a young woman dropped acid. She sat next to a man in a suit: Sidney Cohen, the researcher who had given her the LSD. The woman wore lipstick and nail polish, and her eyes were shining. "I wish I could talk in Technicolor," she said. And, at another point, "I can see the molecules. Were some families maybe--oh, I don't know--eating meat loaf on TV trays as they watched this nice lady undergo her mind-bending, molecule-revealing journey through inner space? Did they switch to "Father Knows Best" or "The Perry Como Show" afterward? One of the feats that the historian Benjamin Breen pulls off in his lively and engrossing new book, "Tripping on Utopia: Margaret Mead, the Cold War, and the Troubled Birth of Psychedelic Science" (Grand Central), is to make a cultural moment like the anonymous woman's televised trip seem less incongruous, if no less ...

Drugs play a central role in modern medicine, but bringing new ones to market is a lengthy, expensive process. Pharmaceutical companies are exploring ways to streamline all aspects of their complex pipelines with artificial intelligence (AI). A key early step is the discovery and design of new molecules that have a desired biochemical effect that can modulate known disease-related processes. To succeed, the molecules must also be suitable for manufacture and drug formulation, and have an acceptably low number of side effects. Finding better candidates and eliminating losers at an early stage makes this process faster and cheaper.

Offering no criteria by which to define unacceptable risk for AI systems and no method to add new high-risk applications to the Act if such applications are discovered to pose a substantial danger of harm. This is particularly problematic because AI systems are becoming broader in their utility. Only requiring that companies take into account harm to individuals, excluding considerations of indirect and aggregate harms to society. An AI system that has a very small effect on, e.g., each person's voting patterns might in the aggregate have a huge social impact. Permitting virtually no public oversight over the assessment of whether AI meets the Act's requirements.

We demonstrate a proof-of-concept of a large language model conducting corporate lobbying related activities. An autoregressive large language model (OpenAI's text-davinci-003) determines if proposed U.S. Congressional bills are relevant to specific public companies and provides explanations and confidence levels. For the bills the model deems as relevant, the model drafts a letter to the sponsor of the bill in an attempt to persuade the congressperson to make changes to the proposed legislation. We use hundreds of novel ground-truth labels of the relevance of a bill to a company to benchmark the performance of the model. It outperforms the baseline of predicting the most common outcome of irrelevance. We also benchmark the performance of the previous OpenAI GPT-3 model (text-davinci-002), which was the state-of-the-art model on many academic natural language tasks until text-davinci-003 was recently released. The performance of text-davinci-002 is worse than the simple baseline. Longer-term, if AI begins to influence law in a manner that is not a direct extension of human intentions, this threatens the critical role that law as information could play in aligning AI with humans. Initially, AI is being used to simply augment human lobbyists for a small portion of their daily tasks. However, firms have an incentive to use less and less human oversight over automated assessments of policy ideas and the written communication to regulatory agencies and Congressional staffers. The core question raised is where to draw the line between human-driven and AI-driven policy influence.

Consider the relationship between a regulator (the principal) and a pharmaceutical company (the agent). The pharmaceutical company wishes to sell a product to make a profit, and the FDA wishes to ensure that only efficacious drugs are released to the public. The efficacy of the drug is not known to the FDA, so the pharmaceutical company must run a costly trial to prove efficacy to the FDA. Critically, the statistical protocol used to establish efficacy affects the behavior of a strategic, self-interested pharmaceutical company; a lower standard of statistical evidence incentivizes the pharmaceutical company to run more trials for drugs that are less likely to be effective, since the drug may pass the trial by chance, resulting in large profits. The interaction between the statistical protocol and the incentives of the pharmaceutical company is crucial to understanding this system and designing protocols with high social utility. In this work, we discuss how the principal and agent can enter into a contract with payoffs based on statistical evidence. When there is stronger evidence for the quality of the product, the principal allows the agent to make a larger profit. We show how to design contracts that are robust to an agent's strategic actions, and derive the optimal contract in the presence of strategic behavior.

To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n{\deg}831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries which are described in a companion paper.

Artificial intelligence and other advances in technology are having a huge impact on every sector of human activity. New ways of researching, manufacturing, commercialising and delivering products and services appear every day, with more than a few paradigm-shifting technological breakthroughs seemingly on the horizon. The intersection of technology and medicine has always been an important one. The way we identify, diagnose and treat diseases is fundamentally linked to technology. In this article, I examine three key ways technology, and especially artificial intelligence (AI) and machine learning (ML), are changing the face of Pharma.

The shift in pharmaceutical sales from traditional, product-driven approaches to customer-centric ones is well underway. Many pharmaceutical companies are now using some kind of AI-powered Next Best Action (NBA) approach to guide marketing and sales efforts, and omnichannel is becoming the industry standard. These approaches benefit customers, be they payers, practitioners or patients, who get to enjoy a more personalised and customer-centric experience, tailored to their preferred methods of communication. When done correctly, AI-powered NBA and Omnichannel in pharma leads to increased sales, improved retention, and greater overall customer satisfaction. In fact, customers have responded so well to NBA and omnichannel practices that, in just a few short years, it has become an integral part of their expectations.

The proportion of pharmaceutical companies hiring for artificial intelligence related positions dropped in October 2022 compared with the equivalent month last year, with 37.4% of the companies included in our analysis recruiting for at least one such position. This latest figure was lower than the 38.6% of companies who were hiring for artificial intelligence related jobs a year ago and a decrease compared to the figure of 39.8% in September 2022. When it came to the rate of all job openings that were linked to artificial intelligence, related job postings dropped in October 2022 from September 2022, with 7% of newly posted job advertisements being linked to the topic. This latest figure was an increase compared to the 3.3% of newly advertised jobs that were linked to artificial intelligence in the equivalent month a year ago. Artificial intelligence is one of the topics that GlobalData, from whom our data for this article is taken, have identified as being a key disruptive force facing companies in the coming years.