A materials science-inspired paradigm to predict the physical stability of amorphous drugs

Amorphous drugs have gained attention as a promising alternative to crystalline formulations due to their ability to enhance solubility. However, ensuring the physical stability of amorphous drugs is critical for successful commercialisation. Unfortunately, predicting the timescale of crystallisation for amorphous drugs is challenging. To address this problem, machine learning models can be developed to predict the physical stability of amorphous drugs. This study presents methodological advancements in using molecular dynamics simulations to develop machine learning models for predicting the crystallisation tendency of amorphous drugs. The study develops and computes solid-state descriptors that capture the dynamic properties of the amorphous phase and complements the traditional singlemolecule descriptors commonly used in quantitative structure-activity relationship (QSAR) models. We have also specifically focused on a particular molecular glass to gain insights into the dynamical properties of materials similar to amorphous drugs. The results show that the use of molecular simulations as a tool to enrich the traditional machine learning paradigm for drug design and discovery can lead to high accuracy in predicting the physical stability of amorphous drugs. The net result of this work is an improvement over the state-of-the-art in predicting the crystallisation tendency of amorphous drugs

Combining machine learning and molecular simulations to predict the stability of amorphous drugs

Amorphous drugs represent an intriguing option to bypass the low solubility of many crystalline formulations of phar- maceuticals. The physical stability of the amorphous phase with respect to the crystal is crucial to bring amorphous formulations into the market - however, predicting the timescale involved with the onset of crystallisation a priori is a formidably challenging task. Machine learning can help in this context, by crafting models capable of predicting the physical stability of any given amorphous drug. In this work, we leverage the outcomes of molecular dynamics sim- ulations to further the state-of-the-art. In particular, we devise, compute and use ”solid state” descriptors that capture the dynamical properties of the amorphous phases, thus complementing the picture offered by the ”traditional”, ”one- molecule” descriptors used in most quantitative structure–activity relationship models (QSAR) models. The results in terms of accuracy are very encouraging, and demonstrate the added value of using molecular simulations as a tool to enrich the traditional machine learning paradigm for I. INTRODUCTION Most modern pharmaceutical drugs are packaged as crys- talline formulations1. The crystalline structure has signifi- cant effects on several physical properties of the drug, such as its solubility, its stability and its bioavailability2. Cru- cially, almost 90% of pharmaceutical drugs are categorised as poorly water soluble3,4, which clearly limits their effective- ness, chiefly in terms of bioavailability. Packaging pharmaceutical drugs as amorphous formula- tions represents a viable way forward in order to improve the solubility of modern drug formulations5, as they present sev- eral benefits in comparison to crystalline drugs. Firstly, most amorphous compounds are intrinsically much more soluble than their crystalline counterparts6–8. As such, amorphous drugs typically act more quickly than crystalline drugs9,10. In addition, amorphous drugs can be more easily packaged into different formulations - such as tablets, capsules, or suspen- sions8,11. In fact, the lack of crystalline structure can also al- low for greater flexibility in designing drug delivery systems with specific properties, such as sustained release or targeted delivery8 . While amorphous drugs appear to have an edge over their crystalline counterparts, they also have some disadvantages that can make their development and formulation challenging - chiefly their lack of stability. Amorphous solids are almost always metastable with respect to their crystalline phases, which means that amorphous drugs have a tendency to crys- tallise12 - within a timescale that is very challenging to predict. This represents a serious problem12, in that the properties of the crystalline form might differ from that of the amorphous phase - which poses a severe clinical risk. In addition, the structural relaxation of the glass alone might alter the func- tional properties of the amorphous formulation13. It is also important to note that the production of amorphous drugs can a)Corresponding author: [email protected] drug design and discovery

Less may be more : an informed reflection on molecular descriptors for drug design and discovery

The phenomenal advances of machine learning in the context of drug design and discovery have led to the development of a plethora of molecular descriptors. In fact, many of these "standard" descriptors are now readily available via open source, easy-to-use computational tools. As a result, it is not uncommon to take advantage of large numbers - up to thousands in some cases - of these descriptors to predict the functional properties of drug-like molecules. This "strength in numbers" approach does usually provide excellent flexibility - and thus, good numerical accuracy - to the machine learning framework of choice; however, it suffers from a lack of transparency, in that it becomes very challenging to pinpoint the - usually, few - descriptors that are playing a key role in determining the functional properties of a given molecule. In this work, we show that just a handful of well-tailored molecular descriptors may often be capable to predict the functional properties of drug-like molecules with an accuracy comparable to that obtained by using hundreds of standard descriptors. In particular, we apply feature selection and genetic algorithms to in-house descriptors we have developed building on junction trees and symmetry functions, respectively. We find that information from as few as 10-20 molecular fragments is often enough to predict with decent accuracy even complex biomedical activities. In addition, we demonstrate that the usage of small sets of optimised symmetry functions may pave the way towards the prediction of the physical properties of drugs in their solid phases - a pivotal challenge for the pharmaceutical industry. Thus, this work brings strong arguments in support of the usage of small numbers of selected descriptors to discover the structure-function relation of drug-like molecules - as opposed to blindly leveraging the flexibility of the thousands of molecular descriptors currently available

Inefficient Toll-Like Receptor-4 Stimulation Enables Bordetella parapertussis to Avoid Host Immunity

The recognition of bacterial lipopolysaccharide (LPS) by host Toll-like receptor (TLR)4 is a crucial step in developing protective immunity against several gram negative bacterial pathogens. Bordetella bronchiseptica and B. pertussis stimulate robust TLR4 responses that are required to control the infection, but a close relative, B. parapertussis, poorly stimulates this receptor, and TLR4 deficiency does not affect its course of infection. This led us to hypothesize that inefficient TLR4 stimulation enables B. parapertussis to evade host immunity. In a mouse model of infection, B. parapertussis grew rapidly in the lungs, but no measurable increase in TLR4-mediated cytokine, chemokine, or leukocyte responses were observed over the first few days of infection. Delivery of a TLR4 stimulant in the inoculum resulted in a robust inflammatory response and a 10- to 100-fold reduction of B. parapertussis numbers. As we have previously shown, B. parapertussis grows efficiently during the first week of infection even in animals passively immunized with antibodies. We show that this evasion of antibody-mediated clearance is dependent on the lack of TLR4 stimulation by B. parapertussis as co-inoculation with a TLR4 agonist resulted in 10,000-fold lower B. parapertussis numbers on day 3 in antibody-treated wild type, but not TLR4-deficient, mice. Together, these results indicate that inefficient TLR4 stimulation by B. parapertussis enables it to avoid host immunity and grow to high numbers in the respiratory tract of naïve and immunized hosts

Broadband multi-wavelength properties of M87 during the 2017 Event Horizon Telescope campaign

High Energy AstrophysicsInstrumentatio

Broadband Multi-wavelength Properties of M87 during the 2017 Event Horizon Telescope Campaign

Abstract: In 2017, the Event Horizon Telescope (EHT) Collaboration succeeded in capturing the first direct image of the center of the M87 galaxy. The asymmetric ring morphology and size are consistent with theoretical expectations for a weakly accreting supermassive black hole of mass ∼6.5 × 109 M ⊙. The EHTC also partnered with several international facilities in space and on the ground, to arrange an extensive, quasi-simultaneous multi-wavelength campaign. This Letter presents the results and analysis of this campaign, as well as the multi-wavelength data as a legacy data repository. We captured M87 in a historically low state, and the core flux dominates over HST-1 at high energies, making it possible to combine core flux constraints with the more spatially precise very long baseline interferometry data. We present the most complete simultaneous multi-wavelength spectrum of the active nucleus to date, and discuss the complexity and caveats of combining data from different spatial scales into one broadband spectrum. We apply two heuristic, isotropic leptonic single-zone models to provide insight into the basic source properties, but conclude that a structured jet is necessary to explain M87’s spectrum. We can exclude that the simultaneous γ-ray emission is produced via inverse Compton emission in the same region producing the EHT mm-band emission, and further conclude that the γ-rays can only be produced in the inner jets (inward of HST-1) if there are strongly particle-dominated regions. Direct synchrotron emission from accelerated protons and secondaries cannot yet be excluded

Leveraging genetic algorithms to maximise the predictive capabilities of the SOAP descriptor

Acknowledgements: T. B. thanks EPSRC for a PhD studentship through the Mathematics for Real-World Systems Centre for Doctoral Training (MathSys, EPSRC grant number EP/S022244/1). S. T. thanks EPSRC for a PhD studentship through the Centre for Doctoral Training in Modeling of Heterogeneous Systems (HetSys, EPSRC Grant No. EP/S022848/1). A. P. B. is supported by the NOMAD Centre of Excellence (European Commission grant agreement ID 951786) and the CASTEP-USER project, funded by the Engineering and Physical Sciences Research Council under the grant agreement EP/W030438/1. We gratefully acknowledge the use of Athena at HPC Midlands+, which was funded by the EPSRC through Grant No. EP/P020232/1, through the HPC Midlands+ consortium, as well as the high-performance computing facilities provided by the Scientific Computing Research Technology Platform at the University of Warwick.The smooth overlap of atomic positions (SOAP) descriptor represents an increasingly common approach to encode local atomic environments in a form readily digestible to machine learning algorithms.</jats:p

Understanding the emergence of the boson peak in molecular glasses

A common feature of glasses is the “boson peak”, observed as an excess in the heat capacity over the crystal or as an additional peak in the terahertz vibrational spectrum. The microscopic origins of this peak are not well understood; the emergence of locally ordered structures has been put forward as a possible candidate. Here, we show that depolarised Raman scattering in liquids consisting of highly symmetric molecules can be used to isolate the boson peak, allowing its detailed observation from the liquid into the glass. The boson peak in the vibrational spectrum matches the excess heat capacity. As the boson peak intensifies on cooling, wide-angle x-ray scattering shows the simultaneous appearance of a pre-peak due to molecular clusters consisting of circa 20 molecules. Atomistic molecular dynamics simulations indicate that these are caused by over-coordinated molecules. These findings represent an essential step toward our understanding of the physics of vitrification