Our cells function in the continuous mode of producing and degrading proteins, keeping the balance between these two processes to maintain a healthy cellular function. However, sometimes the produced proteins might be defective, accumulating and causing pathological effects. This is one of the examples when hijacking the naturally occurring process of protein degradation becomes highly useful for rescuing the cells.

When the time comes, in the majority of cases ubiquitin-proteasome system (UPS) takes over proteins. The sequence of enzymatic reactions leads to marking doomed proteins by the ubiquitin chain, which is done by E3 ubiquitin ligase. This causes the signaling cascade and, consequently, proteasomal recycling of proteins into the new building blocks.

Scientists figured out a way to force E3 ubiquitin ligase to get in proximity with any protein of interest so that the UPS can be involved in the targeted protein degradation -- by applying "molecular glues" or proteolysis-targeting chimeras, or PROTACs, the two most popular strategies to go about protein degradation. 

Are there alternatives to using animals in drug discovery? Even though right now it still seems like a milestone which is almost impossible to avoid, there is a way to not only save the animal lives, but also enhance the drug discovery research by introducing more human-relevant physiological systems. 

In this article we are going to focus our attention around the organ-on-a-chip systems, recent advancements in the sphere and discuss to which extent we can compare such organ chips to the human. In few words, organs-on-chips are constructed from engineered or natural tissues grown inside microfluidic chips, which creates the close-to-physiological cells interplay, mimicking the liquid flow, its pressure and concentration gradients of chemicals.

The standard organs-on-chips are built as cavities containing tissues, connected with channels, where it all together is integrated with electrical connectors, electrodes and microelectrodes. The assembled chip should have a liquid circulating and tissues functioning and “communicating” as it would be occuring in a living body. For example, the heart tissue would contract, liver tissue would produce a set of enzymes, and the “brain” would be protected with a blood-brain barrier.

When we hear “AI in drug discovery”, there is a high chance our imagination would draw a small molecule discovery in the first place. Indeed, as it was captured and analyzed in the report The Landscape of Artificial Intelligence (AI) In Pharmaceutical R&D roughly half of more than 300 analyzed AI-driven companies focus on small molecules as a therapeutic modality. This biggest category is then followed by smaller groups of biologics, biomarkers and therapies as an R&D focus. The success of small molecule discovery coupled with AI already demonstrates performance with some “AI-inspired” drug candidates entering the clinical trials, like Relay Therapeutics with their precision oncology small molecules in clinical trials for previously ‘undruggable’ targets, or Exscientia with their small molecule candidate to treat obsessive-compulsive disorder, which began human testing in a phase I trial in 2020 in collaboration with Sumitomo Dainippon Pharma.

Drug delivery technologies often become a remedy and a solution for various drugs on their way to clinical approval. When the drug is developed, whether it is a small molecule, peptide, nucleic acid, antibody, or even the whole living cell, it is essential to address the possible delivery hurdles and choose the proper strategy to target the right site with the right drug amount.

Knowing the challenges and needs of various drug classes shapes several delivery paradigms for improving a drug’s therapeutic function. The Nature review paper classified the delivery paradigms into three classes: modification of the drug itself; alteration of the environment around the drug; and creation of an interface between the drug and its surrounding. In the last case, such an interface is a drug delivery system, that facilitates delivery by controlling the interactions between the drug and its microenvironment.

Scandinavian countries are known for their active innovation in the worldwide market, including pharmaceutical and biotech fields. Numerous research institutes, start-up incubators, and grant opportunities give rise to active drug development, medical device innovation, and implementation of machine learning (ML) and quantum computing into these processes.

Over the past decade, Canada emerged as a promising global location for drug discovery and biotech business, especially when it comes to applying advanced technologies, such as artificial intelligence (AI) and quantum computing in life sciences.

(Last updated: October 05, 2022)

The interest of pharma organizations in DNA-encoded chemical libraries (DEL) technology has been growing over the years, with numerous pharma organizations now having their internal screening programs using DELs or outsourcing capabilities from specialized DEL providers.

This report provides a bird's view of the DEL market, including a summary of DEL technology benefits and limitations, key players, major research deals, and several examples of successful hit discovery programs using DELs. 

Disclaimer: This post is not investment advice of any kind; it is for informational purposes only. Neither BPT Analytics Ltd nor the authors of this post endorse or recommend any particular stocks or investment opportunities. Consult relevant professional service providers for any investment decisions at all times. 

In a report, The Landscape of Artificial Intelligence (AI) In The Pharmaceutical Research, the community of AI-driven companies in biotech is represented by more than 340 players, focusing on a wide scale of applications from target discovery to clinics. A few companies managed to build sophisticated AI-driven end-to-end drug discovery platforms, allowing them to advance their business faster than competitors. In the last three years, several such companies performed successful IPOs.

The implementation of AI into drug discovery is happening gradually and has already demonstrated multiple advantages, such as accelerated target identification, optimized small molecules, biologics design, better management of clinical trials, and so on. Nowadays, AI helps to not only significantly cut down the R&D costs but also do things that are believed to be impossible using just human brain power. Such tasks might include drug repurposing, novel target identification for the complex nets of molecular pathways, or simulation of a molecule’s toxicity in the human body. 

Below we summarize some of the public companies with AI-powered platforms.

Nanomedicines, as it goes from the name, are nanotechnology-based drugs, used for the treatment, diagnosis or prevention of various diseases. But is “nano” defined just by the size? According to FDA, nanomedicines are the products in the nanoscale range (meaning at least one size dimension being around 1-100 nm) that can exhibit chemical or physical properties, or biological effects which differ compared to larger-scale counterparts. At the same time FDA adds that products outside the mentioned nanoscale range also can be called nanomedicines if they can exhibit similar properties or phenomena attributable to a 1-100 nm scale dimension.

Usually, nanomedicines consist of a carrier and a drug, where the last is also called an active pharmaceutical ingredient (API). However, sometimes the API can be transformed into a nanomedicine itself via manufacturing it in nanoscale size range ( stabilized nanoscale crystals). Notably, the last FDA approval of such nanocrystal drug was in 2015, which is the drug for a rare progressive lung disease. In this article we will focus on some nanomedicines approved by the FDA in the last 5 years, as well as discuss promising ongoing clinical trials.

Auron Therapeutics, a biopharmaceutical company focused on developing therapies that target dysregulated differentiation and cellular plasticity for treating cancer, just announced the completion of its $48 million Series A financing round. Proceeds will be used to advance the lead program toward clinical development and drive additional programs into drug discovery. Funds will also be used to expand the proprietary, machine learning-based computational platform, AURigin, that is used to identify novel drug targets, and add personnel to support and accelerate research and development.