The Emergence of Baricitinib: A Story of Tortoises Versus Hares

Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics

Over the period of the preceding decade, artificial intelligence (AI) has proved an outstanding performance in entire dimensions of science including pharmaceutical sciences. AI uses the concept of machine learning (ML), deep learning (DL), and neural networks (NNs) approaches for novel algorithm and hypothesis development by training the machines in multiple ways. AI-based drug development from molecule identification to clinical approval tremendously reduces the cost of development and the time over conventional methods. The COVID-19 vaccine development and approval by regulatory agencies within 1-2 years is the finest example of drug development. Hence, AI is fast becoming a boon for scientific researchers to streamline their advanced discoveries. AI-based FDA-approved nanomedicines perform well as target selective, synergistic therapies, recolonize the theragnostic pharmaceutical stream, and significantly improve drug research outcomes. This comprehensive review delves into the fundamental aspects of AI along with its applications in the realm of pharmaceutical life sciences. It explores AI's role in crucial areas such as drug designing, drug discovery and development, traditional Chinese medicine, integration of multi-omics data, as well as investigations into drug repurposing and polypharmacology studies.

Therapeutic and anti-inflammatory effects of baricitinib on mortality, ICU transfer, clinical improvement, and CRS-related laboratory parameters of hospitalized patients with moderate to severe COVID-19 pneumonia: a systematic review and meta-analysis

BACKGROUND

Due to the high incidence and mortality of the worldwide COVID-19 pandemic, beneficial effects of effective antiviral and anti-inflammatory drugs used in other diseases, especially rheumatic diseases, were observed in the treatment of COVID-19.

METHODS

Clinical and laboratory parameters of eight included cohort studies and five Randomized Control Trials between the baricitinib group and the control group were analyzed on the first day of admission and days 7, 14, and 28 during hospitalization.

RESULTS

According to the meta-analysis result of eight included cohort studies with 2088 patients, the Pooled Risk Ratios were 0.46 (P<0.001) for mortality, 6.14 (P< 0.001) for hospital discharge, and the mean differences of 76.78 (P< 0.001) for PaO₂/FiO₂ ratio was -47.32 (P= 0.02) for CRP, in the baricitinib group vs. control group on the seventh or fourteenth day of the treatment compared to the first day. Based on the meta-analysis of five RCT studies with 11825 patients, the pooled RR was 0.84 (P= 0.001) for mortality and 1.07 (P= 0.014) for patients' recovery. The mean differences were -0.80 (P<0.001) for hospitalization days, -0.51(P= 0.33) for time to recovery in the baricitinib group vs. control group.

CONCLUSIONS

Baricitinib prescription is strongly recommended in moderate to severe COVID-19.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration number: CRD42021254541.

Computational and Experimental Approaches Identify Beta-Blockers as Potential SARS-CoV-2 Spike Inhibitors

Finding antivirals for SARS-CoV-2 is still a major challenge, and many computational and experimental approaches have been employed to find a solution to this problem. While the global vaccination campaigns are the primary driver of controlling the current pandemic, orally bioavailable small-molecule drugs and biologics are critical to overcome this global issue. Improved therapeutics and prophylactics are required to treat people with circulating and emerging new variants, addressing severe infection, and people with underlying or immunocompromised conditions. The SARS-CoV-2 envelope spike is a challenging target for viral entry inhibitors. Pindolol presented a good docking score in a previous virtual screening using computational docking calculations after screening a Food and Drug Administration (FDA)-approved drug library of 2400 molecules as potential candidates to block the SARS-CoV-2 spike protein interaction with the angiotensin-converting enzyme 2 (ACE-2). Here, we expanded the computational evaluation to identify five beta-blockers against SARS-CoV-2 using several techniques, such as microscale thermophoresis, NanoDSF, and in vitro assays in different cell lines. These data identified carvedilol with a K _d of 364 ± 22 nM for the SARS-CoV-2 spike and in vitro activity (EC₅₀ of 7.57 μM, CC₅₀ of 18.07 μM) against SARS-CoV-2 in Calu-3 cells. We have shown how we can apply multiple computational and experimental approaches to find molecules that can be further optimized to improve anti-SARS-CoV-2 activity.

Recent advances in drug repurposing using machine learning

Drug repurposing aims to find new uses for already existing and approved drugs. We now provide a brief overview of recent developments in drug repurposing using machine learning alongside other computational approaches for comparison. We also highlight several applications for cancer using kinase inhibitors, Alzheimer's disease as well as COVID-19.