How Recent Drug Pricing Laws Impact Drug Discovery (H.R. 5376)

The final set of Molecules of the Month for 2023 is out, and now it’s time to nominate your favorite molecules for 2023’s Molecules of the Year! Nominees should have either been first disclosed in 2023 or had a major new update in 2023 (e.g. clinical trial readout, publication, business transaction, approval). The molecule must have a [...]

The "New Drugs on the Horizon" sessions at the AACR Annual Meeting 2024 in San Diego revealed twelve innovative oncology agents and offered attendees a first look at their structures and preliminary data as they enter/progress in the clinic. In case you missed any of these exciting compounds – including selective CDK inhibitors, molecular glues, bifunctional degraders, a radiopharmaceutical, a bifunctional antibody, and an ADC – this article covers the structures and targets disclosed. A separate session disclosed the structure of a pan-RAS isoform inhibitor with remarkable clinical results.

The impact of small and mid-sized biotech companies has become increasingly important. The shift from large pharma dominance to a biotech-driven era of drug discovery presents challenges to the traditional model of education for emerging medicinal chemists. Industry leader Dean Brown recently highlighted challenges for modern medicinal chemists starting in biotech who face fewer mentoring opportunities and are often propelled into leadership positions sooner than their counterparts in big pharma. Here we share how our team of former industry scientists at Drug Hunter can help.

We asked the global drug discovery community to nominate and vote on their favorite molecule from 2023, and the results are in. The 2023 winner, with the most votes between ten molecules, is Merck’s oral macrocyclic peptide inhibitor of PCSK9, MK-0616, derived from mRNA display technology and shown to have clinical efficacy. Herein, we highlight what makes MK-0616 so impressive to the drug discovery community.

One of the key challenges in CNS projects is achieving sufficient unbound brain exposure to engage the target. To predict CNS exposure, scientists employ metrics like CNS MPO scores from Pfizer and Merck, Bayesian models, and methods for balancing physicochemical properties. However, these in silico metrics often fail to correlate well with experimental data. Schrödinger's team has discovered the energy of solvation (E-sol) metric, which accurately predicts Kp,uu and has been validated with CNS-penetrant DLK inhibitors. Explore more in this detailed Drug Hunter article.