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The current work aims to design and provide a preliminary IND-enabling study of selective BMX inhibitors for cancer therapeutics development. BMX is an emerging target, more notably in oncological and immunological diseases. In this work, we have employed a predictive AI-based platform to design the selective inhibitors considering the novelty, IP prior protection, and drug-likeness properties. Furthermore, selected top candidates from the initial iteration of the design were synthesized and chemically characterized utilizing 1H NMR and LC-MS. Employing a panel of biochemical (enzymatic) and cancer cell lines, the selected molecules were tested against these assays. In addition, we used artificial intelligence to predict and evaluate several critical IND-focused physicochemical and pharmacokinetics values of the selected molecules. A secondary objective of the current work was also to validate the sole role of BMX in animal models known to be mediated by BMX. More than 50 molecules were designed in the present study employing five novel discovered scaffolds. Two molecules were nominated for further IND-focused studies. Compound II showed promising in-vitro activity against BMX in both enzymatic assays compared to other kinases and in cancer cell lines with known BMX overexpression. Interestingly, compound II showed very favorable physicochemical and pharmacokinetics properties as predicted by the used platforms. The animal study further confirmed the sole role of BMX in the disease model. The current work provides promising data on a selective BMX inhibitor as a potential lead for therapeutics development, and the asset is currently in the optimization stage. Notably, the current study shows a framework for a combined approach employing both AI and experimentation that can be used by academic labs in their research programs to more streamline programs into IND-focused to be bridged easily for further clinical development with industrial partners.

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PURPOSE: GSK2982772 is a selective inhibitor of receptor-interacting protein kinase-1 (RIPK1) with a short 2- to 3-h half-life. In a previous modified-release (MR) study, a matrix monolithic formulation (80% GSK2982772 released over 12 h) provided a once-daily (QD) pharmacokinetic (PK) profile in the fasted state; however, it was susceptible to food effects. The current study evaluated the safety and PK of MR formulations using GSK proprietary DiffCORE™ technology. METHODS: Part A evaluated PK following single-dose (240 mg) fasted and fed (high-fat meal) administration of three DiffCORE MR formulations within pre-defined in vitro extremes of 80% GSK2982772 released over 12 h (MR-12 h) to 80% GSK2982772 released over 18 h (MR-18 h) versus an immediate-release formulation. Part B evaluated MR-16 h (120-960 mg) in different prandial states. RESULTS: Pharmacokinetic profiles for all MR formulations and doses tested in the fasted and fed states were consistent with QD dosing. CONCLUSIONS: The DiffCORE technology overcame the food effect vulnerability observed with the matrix monolithic formulation. The MR-16 h formulation was selected for further clinical development as a QD dosing regimen (NCT03649412 September 26, 2018).

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For the last 40 years, praziquantel has been the standard treatment for schistosomiasis, a neglected parasitic disease affecting more than 250 million people worldwide. However, there is no suitable paediatric formulation on the market, leading to off-label use and the splitting of commercial tablets for adults. In this study, we use a recently available technology, direct powder extrusion (DPE) three-dimensional printing (3DP), to prepare paediatric Printlets™ (3D printed tablets) of amorphous solid dispersions of praziquantel with Kollidon® VA 64 and surfactants (Span™ 20 or Kolliphor® SLS). Printlets were successfully printed from both pellets and powders obtained from extrudates by hot melt extrusion (HME). In vitro dissolution studies showed a greater than four-fold increase in praziquantel release, due to the formation of amorphous solid dispersions. In vitro palatability data indicated that the printlets were in the range of praziquantel tolerability, highlighting the taste masking capabilities of this technology without the need for additional taste masking excipients. This work has demonstrated the possibility of 3D printing tablets using pellets or powder forms obtained by HME, avoiding the use of filaments in fused deposition modelling 3DP. Moreover, the main formulation hurdles of praziquantel, such as low drug solubility, inadequate taste, and high and variable dose requirements, can be overcome using this technology.

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3D printing (3DP) is a progressive technology capable of transforming pharmaceutical development. However, despite its promising advantages, its transition into clinical settings remains slow. To make the vital leap to mainstream clinical practice and improve patient care, 3DP must harness modern technologies. Machine learning (ML), an influential branch of artificial intelligence, may be a key partner for 3DP. Together, 3DP and ML can utilise intelligence based on human learning to accelerate drug product development, ensure stringent quality control (QC), and inspire innovative dosage-form design. With ML's capabilities, streamlined 3DP drug delivery could mark the next era of personalised medicine. This review details how ML can be applied to elevate the 3DP of pharmaceuticals and importantly, how it can expedite 3DP's integration into mainstream healthcare.

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Artificial intelligence (AI) is redefining how we exist in the world. In almost every sector of society, AI is performing tasks with super-human speed and intellect; from the prediction of stock market trends to driverless vehicles, diagnosis of disease, and robotic surgery. Despite this growing success, the pharmaceutical field is yet to truly harness AI. Development and manufacture of medicines remains largely in a 'one size fits all' paradigm, in which mass-produced, identical formulations are expected to meet individual patient needs. Recently, 3D printing (3DP) has illuminated a path for on-demand production of fully customisable medicines. Due to its flexibility, pharmaceutical 3DP presents innumerable options during formulation development that generally require expert navigation. Leveraging AI within pharmaceutical 3DP removes the need for human expertise, as optimal process parameters can be accurately predicted by machine learning. AI can also be incorporated into a pharmaceutical 3DP 'Internet of Things', moving the personalised production of medicines into an intelligent, streamlined, and autonomous pipeline. Supportive infrastructure, such as The Cloud and blockchain, will also play a vital role. Crucially, these technologies will expedite the use of pharmaceutical 3DP in clinical settings and drive the global movement towards personalised medicine and Industry 4.0.

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Three-dimensional (3D) printing is a revolutionary technology that is disrupting pharmaceutical development by enabling the production of personalised printlets (3D printed drug products) on demand. By creating small batches of dose flexible medicines, this versatile technology offers significant advantages for clinical practice and drug development, namely the ability to personalise medicines to individual patient needs, as well as expedite drug development timelines within preclinical studies through to first-in-human (FIH) and Phase I/II clinical trials. Despite the widely demonstrated benefits of 3D printing pharmaceuticals, the clinical potential of the technology is yet to be realised. In this timely review, we provide an overview of the latest cutting-edge investigations in 3D printing pharmaceuticals in the pre-clinical and clinical arena and offer a forward-looking approach towards strategies to further aid the translation of 3D printing into the clinic.

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Traditional formulation development studies involve expensive and time-consuming screening of prototypes in preclinical species to select 'lead' systems for evaluation in human clinical pharmacokinetic studies. A new paradigm, Translational Pharmaceutics, has emerged to integrate pharmaceutical development, manufacturing and clinical functions to address these restrictions. Rapid Formulation development and Clinical Testing (RapidFACT) is applied to exploit the benefits of Translational Pharmaceutics in the clinical screening and optimization of drug products. Benefits are maximized by the adapted utilization of the concept of 'formulation design space'. This article presents the experience of the application of design space within RapidFACT and is supported by data from over 200 formulations studied to date, including case studies on how the approach has been applied.

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