SynToxProfiler: An interactive analysis of drug combination synergy, toxicity and efficacy

Combinations of approved oral nucleoside analogues confer potent suppression of alphaviruses in vitro and in vivo

Alphaviruses, including chikungunya virus (CHIKV), pose a significant global health threat, yet specific antiviral therapies remain unavailable. We evaluated combinations of three oral directly acting antiviral drugs (sofosbuvir (SOF), molnupiravir (MPV), and favipiravir (FAV)), which are approved for other indications, against CHIKV, Semliki Forest virus (SFV), Sindbis virus (SINV), and Venezuelan Equine Encephalitis virus (VEEV) in vitro and in vivo. We assessed antiviral efficacy in human skin fibroblasts and liver cells, as well as in a mouse model of CHIKV-induced arthritis. In human skin fibroblasts, synergistic antiviral effects were observed for combinations of MPV + SOF and FAV + SOF against CHIKV, and for FAV + SOF against SFV. In human liver cells, FAV + MPV conferred additive to synergistic activity against VEEV and SINV, while SOF synergized with FAV against SINV. In mice, MPV improved CHIKV-induced foot swelling and reduced systemic infectious virus titres. Combination treatment with MPV and SOF significantly reduced swelling and infectious titres compared to monotherapies of each drug. Sequencing of CHIKV RNA from joint tissue revealed that MPV caused dose-dependent increases in mutations in the CHIKV genome. Upon combination therapy of MPV with SOF, the number of mutations was significantly lower compared to monotherapy with several higher doses of MPV. Combining these approved oral nucleoside analogues confers potent suppression of multiple alphaviruses in vitro and in vivo with enhanced control of viral genetic evolution in face of antiviral pressure. These drug combinations may ultimately lead to the development of potent combinations of pan-family alphavirus inhibitors.

Dual STAT3/STAT5 inhibition as a novel treatment strategy in T-prolymphocytic leukemia

T-prolymphocytic leukemia (T-PLL) is a rare, aggressive T-cell malignancy with poor outcomes and an urgent need for new therapeutic approaches. Integrating genomic data and new transcriptomic profiling, we identified recurrent JAK/STAT mutations (predominantly in JAK3 and STAT5B) as hallmarks in a cohort of 335 T-PLL cases. In line, transcriptomic and protein analyses revealed constitutive JAK/STAT activation in virtually all samples. Consequently, we explored the anti-leukemic potential of dual STAT3/STAT5 non-PROTAC degraders in T-PLL, with JPX-1244 as our lead substance. JPX-1244 efficiently and selectively induced cell death in primary T-PLL samples, including those resistant to conventional therapies, by blocking STAT3 and STAT5 phosphorylation and by inducing their degradation. The extent of STAT3/STAT5 degradation directly correlated with cytotoxicity. RNA-sequencing confirmed the treatment-related downregulation of STAT5 target genes. While JAK/STAT mutations did not predict responses to pharmacologic STAT3/STAT5 degradation, elevated expression of TOX, PAK6, and SPINT1 were associated with drug sensitivity. In subsequent combination screenings, cladribine, venetoclax, and azacytidine emerged as most effective combination partners of STAT3/STAT5 degraders, even in low-responding T-PLL samples, all synergistically reducing STAT5 phosphorylation. These findings highlight dual STAT3/STAT5 inhibition, particularly in combination with hypomethylating and BCL2-targeting agents, as a promising interventional approach in T-PLL, warranting further investigation.

Exome-wide association study reveals 7 functional variants associated with ex-vivo drug response in acute myeloid leukemia patients

Acute myeloid leukemia (AML) is an aggressive blood cancer characterized by poor survival outcomes. Further, due to the extreme molecular heterogeneity of the disease, drug treatment response varies from patient to patient. The variability of drug response can cause unnecessary treatment in more than half of the patients with no or partial therapy responses leading to severe side effects, monetary as well as time loss. Understanding the genetic risk factors underlying the drug response in AML can help with improved prediction of treatment responses and identification of biomarkers in addition to mechanistic insights to monitor treatment response. Here, we report the results of the first Exome-Wide Association Study (EWAS) of ex-vivo drug response performed to date with 175 AML cases and 47 drugs. We used information from 55,423 germline exonic SNPs to perform the analysis. We identified exome-wide significant (p < 9.02 × 10- 7) associations for rs113985677 in CCIN with tamoxifen response, rs115400838 in TRMT5 with idelalisib response, rs11878277 in HDGFL2 with entinostat, and rs2229092 in LTA associated with vorinostat response. Further, using multivariate genome-wide association analysis, we identified the association of rs11556165 in ATRAID, and rs11236938 in TSKU with the combined response of all 47 drugs and 29 nonchemotherapy drugs at the genome-wide significance level (p < 5 × 10- 8). Additionally, a significant association of rs35704242 in NIBAN1 was associated with the combined response for nonchemotherapy medicines (p = 2.51 × 10- 8), and BI.2536, gefitinib, and belinostat were identified as the central traits. Our study represents the first EWAS to date on ex-vivo drug response in AML and reports 7 new associated loci that help to understand the anticancer drug response in AML patients.

Combinations of approved oral nucleoside analogues confer potent suppression of alphaviruses in vitro and in vivo

Background

Alphaviruses, including chikungunya virus (CHIKV), pose a significant global health threat, yet specific antiviral therapies remain unavailable.

Methods

We evaluated combinations of three oral directly acting antiviral drugs (sofosbuvir (SOF), molnupiravir (MPV), and favipiravir (FAV)), which are approved for other indications, against CHIKV, Semliki Forest virus (SFV), Sindbis virus (SINV), and Venezuelan Equine Encephalitis virus (VEEV) in vitro and in vivo . We assessed antiviral efficacy in human skin fibroblasts and liver cells, as well as in a mouse model of CHIKV-induced arthritis.

Findings

In human skin fibroblasts, synergistic antiviral effects were observed for combinations of MPV + SOF and FAV + SOF against CHIKV, and for FAV + SOF against SFV. In human liver cells, FAV + MPV conferred additive to synergistic activity against VEEV and SINV, while SOF synergized with FAV against SINV. In mice, MPV improved CHIKV-induced foot swelling and reduced systemic infectious virus titres. Combination treatment with MPV and SOF significantly reduced swelling and infectious virus titres compared to monotherapies of each drug. Sequencing of CHIKV RNA from joint tissue revealed that MPV caused dose- dependent increases in mutations in the CHIKV genome. Upon combination therapy of MPV with SOF, the number of mutations was significantly lower compared to monotherapy with several higher doses of MPV.

Interpretation

Combining these approved oral nucleoside analogues confers potent suppression of multiple alphaviruses in vitro and in vivo with enhanced control of viral genetic evolution in face of antiviral pressure. These drug combinations may ultimately lead to the development of potent combinations of pan-family alphavirus inhibitors.

Funding

This work was supported by a PhD fellowship granted to S.V. by the Research Foundation - Flanders (FWO) (11D5923N). L.D.C. was also supported by Research Foundation - Flanders (FWO) PhD fellowship (11L1325N). Dr. Polyak and Schiffer are partially supported by R01AI121129.

Research in Context

Evidence before this study: Alphaviruses such as chikungunya virus (CHIKV), Sindbis virus (SINV), and Venezuelan Equine Encephalitis virus (VEEV) are a major threat for global health. Alphaviruses are responsible for debilitating diseases with major public health implications, yet no antiviral drugs are currently approved for treating these virus infections. Existing treatment options are limited to supportive care and are unlikely to provide protection against future outbreaks of other alphaviruses. Previous studies have shown that oral approved nucleoside analogues such as favipiravir (FAV), molnupiravir (MPV), and sofosbuvir (SOF) have antiviral activity against certain RNA viruses, including alphaviruses. However, systematic in vivo evaluations of these drugs and testing of drug combinations in both in vitro and in vivo settings are limited. Added value of this study: This study provides a comprehensive evaluation of combinations of FAV, MPV and SOF against multiple alphaviruses in two human cell lines and a CHIKV mouse model. We demonstrate that certain combinations of these drugs confer synergistic antiviral activity, effectively suppressing CHIKV, SFV, SINV, and VEEV replication in vitro . Moreover, in vivo , we show for the first time that MPV treatment results in reduced CHIKV-induced foot swelling and systemic virus replication. Combining MPV with SOF enhances antiviral activity in mice as compared to monotherapy. By sequencing the viral genome, we show that MPV increases the number of mutations in a dose-dependent manner. Combination therapy of MPV and SOF reduces the number of mutations compared to higher doses of MPV. These findings highlight the potential of nucleoside analogue combinations as a promising therapeutic strategy against alphavirus infections. Implications of all the available evidence: The results of this study suggest that combination therapy with approved nucleoside analogues could provide an effective treatment strategy for alphavirus infections. The observed increased efficacy of drug combinations supports the potential for dose optimization to enhance efficacy while reducing toxicity and development of resistance. Future research should focus on clinical evaluation of these drug combinations, pharmacokinetic studies, and further exploration of their impact on viral evolution. Given the expanding geographical distribution of alphaviruses and the lack of available treatments, these findings provide a foundation for developing pan-alphavirus antiviral therapies that could improve patient outcomes and global outbreak preparedness.

Network-based drug repurposing identifies small molecule drugs as immune checkpoint inhibitors for endometrial cancer

Endometrial cancer (EC) is the 6th most common cancer in women around the world. Alone in the United States (US), 66,200 new cases and 13,030 deaths are expected to occur in 2023 which needs the rapid development of potential therapies against EC. Here, a network-based drug-repurposing strategy is developed which led to the identification of 16 FDA-approved drugs potentially repurposable for EC as potential immune checkpoint inhibitors (ICIs). A network of EC-associated immune checkpoint proteins (ICPs)-induced protein interactions (P-ICP) was constructed. As a result of network analysis of P-ICP, top key target genes closely interacting with ICPs were shortlisted followed by network proximity analysis in drug-target interaction (DTI) network and pathway cross-examination which identified 115 distinct pathways of approved drugs as potential immune checkpoint inhibitors. The presented approach predicted 16 drugs to target EC-associated ICPs-induced pathways, three of which have already been used for EC and six of them possess immunomodulatory properties providing evidence of the validity of the strategy. Classification of the predicted pathways indicated that 15 drugs can be divided into two distinct pathway groups, containing 17 immune pathways and 98 metabolic pathways. In addition, drug-drug correlation analysis provided insight into finding useful drug combinations. This fair and verified analysis creates new opportunities for the quick repurposing of FDA-approved medications in clinical trials.

Optimizing drug combinations for T-PLL: restoring DNA damage and P53-mediated apoptotic responses

ABSTRACT

T-prolymphocytic leukemia (T-PLL) is a mature T-cell neoplasm associated with marked chemotherapy resistance and continued poor clinical outcomes. Current treatments, that is, the CD52-antibody alemtuzumab, offer transient responses, with relapses being almost inevitable without consolidating allogeneic transplantation. Recent more detailed concepts of T-PLL's pathobiology fostered the identification of actionable vulnerabilities: (1) altered epigenetics, (2) defective DNA damage responses, (3) aberrant cell-cycle regulation, and (4) deregulated prosurvival pathways, including T-cell receptor and JAK/STAT signaling. To further develop related preclinical therapeutic concepts, we studied inhibitors of histone deacetylases ([H]DACs), B-cell lymphoma 2 (BCL2), cyclin-dependent kinase (CDK), mouse double minute 2 (MDM2), and classical cytostatics, using (1) single-agent and combinatorial compound testing in 20 well-characterized and molecularly profiled primary T-PLL (validated by additional 42 cases) and (2) 2 independent murine models (syngeneic transplants and patient-derived xenografts). Overall, the most efficient/selective single agents and combinations (in vitro and in mice) included cladribine, romidepsin ([H]DAC), venetoclax (BCL2), and/or idasanutlin (MDM2). Cladribine sensitivity correlated with expression of its target RRM2. T-PLL cells revealed low overall apoptotic priming with heterogeneous dependencies on BCL2 proteins. In additional 38 T-cell leukemia/lymphoma lines, TP53 mutations were associated with resistance toward MDM2 inhibitors. P53 of T-PLL cells, predominantly in wild-type configuration, was amenable to MDM2 inhibition, which increased its MDM2-unbound fraction. This facilitated P53 activation and downstream signals (including enhanced accessibility of target-gene chromatin regions), in particular synergy with insults by cladribine. Our data emphasize the therapeutic potential of pharmacologic strategies to reinstate P53-mediated apoptotic responses. The identified efficacies and their synergies provide an informative background on compound and patient selection for trial designs in T-PLL.

Ovicidal activity of diaryl dichalcogenides and ivermectin on Fasciola hepatica: A novel candidate for a blending-based therapeutic strategy

Fasciolosis is a food and waterborne disease caused by Fasciola spp., representing a global health burden to various hosts, including humans and other animals. This study investigates the in vitro activity of tellurium- and selenium-containing diaryl dichalcogenides: diacetal ditelluride (LQ07), diacetal diselenide (LQ62), and diacetyl diselenide (LQ68) alone and in combination with ivermectin (IVM) against eggs of Fasciola hepatica. The eggs were exposed for 12 h with each organochalcogen (OC) (0.1 - 2 mmol l-1) and IVM (0.01 - 2 mmol l-1) following an incubation of 15 days, allowing embryonation. The inhibitory concentration of 50 % (IC50) of each OC or IVM was tested with the IC10, IC30, and IC50 of IVM or each OC, respectively. LQ07, LQ62, and LQ68, as well as IVM, demonstrated a concentration-dependent ovicidal activity. The peak ovicidal activity of 99.74 % was achieved when IVM was tested at 2.0 mmol l-1. LQ62 and LQ68 demonstrated greater ovicidal activity, having an IC50 < 0.32 mmol l-1 being 6.25-fold more toxic than IVM alone. The percentage of dead eggs was significantly higher in the IVM group (early mortality), as Se-containing OCs led to the (miracidia) embryonation of the eggs with no hatching (late mortality). Blending Se-containing OCs and IVM showed an additive effect of up to 27 % against F. hepatica eggs. The present data contribute to the potential use of blending-based therapeutic strategies to combat F. hepatica infections in eradication programs worldwide. The combinations may also act against multidrug-resistant strains, reinstating drug-based parasite control.

Challenges in validation of combination treatment strategies for CRC using patient-derived organoids

Patient-derived organoids (PDOs) established from tissues from various tumor types gave the foundation of ex vivo models to screen and/or validate the activity of many cancer drug candidates. Due to their phenotypic and genotypic similarity to the tumor of which they were derived, PDOs offer results that effectively complement those obtained from more complex models. Yet, their potential for predicting sensitivity to combination therapy remains underexplored. In this review, we discuss the use of PDOs in both validation and optimization of multi-drug combinations for personalized treatment strategies in CRC. Moreover, we present recent advancements in enriching PDOs with diverse cell types, enhancing their ability to mimic the complexity of in vivo environments. Finally, we debate how such sophisticated models are narrowing the gap in personalized medicine, particularly through immunotherapy strategies and discuss the challenges and future direction in this promising field.

A pan-cancer screen identifies drug combination benefit in cancer cell lines at the individual and population level

Combining drugs can enhance their clinical efficacy, but the number of possible combinations and inter-tumor heterogeneity make identifying effective combinations challenging, while existing approaches often overlook clinically relevant activity. We screen one of the largest cell line panels (N = 757) with 51 clinically relevant combinations and identify responses at the level of individual cell lines and tissue populations. We establish three response classes to model cellular effects beyond monotherapy: synergy, Bliss additivity, and independent drug action (IDA). Synergy is rare (11% of responses) and frequently efficacious (>50% viability reduction), whereas Bliss and IDA are more frequent but less frequently efficacious. We introduce "efficacious combination benefit" (ECB) to describe high-efficacy responses classified as either synergy, Bliss, or IDA. We identify ECB biomarkers in vitro and show that ECB predicts response in patient-derived xenografts better than synergy alone. Our work here provides a valuable resource and framework for preclinical evaluation and the development of combination treatments.

Exploring cutting-edge strategies for drug repurposing in female cancers - An insight into the tools of the trade

Female cancers, which include breast and gynaecological cancers, represent a significant global health burden for women. Despite advancements in research pertinent to unearthing crucial pathological characteristics of these cancers, challenges persist in discovering potential therapeutic strategies. This is further exacerbated by economic burdens associated with de novo drug discovery and clinical intricacies such as development of drug resistance and metastasis. Drug repurposing, an innovative approach leveraging existing FDA-approved drugs for new indications, presents a promising avenue to expedite therapeutic development. Computational techniques, including virtual screening and analysis of drug-target-disease relationships, enable the identification of potential candidate drugs. Integration of diverse data types, such as omics and clinical information, enhances the precision and efficacy of drug repurposing strategies. Experimental approaches, including high-throughput screening assays, in vitro, and in vivo models, complement computational methods, facilitating the validation of repurposed drugs. This review highlights various target mining strategies based on analysis of differential gene expression, weighted gene co-expression, protein-protein interaction network, and host-pathogen interaction, among others. To unearth drug candidates, the technicalities of leveraging information from databases such as DrugBank, STITCH, LINCS, and ChEMBL, among others are discussed. Further in silico validation techniques encompassing molecular docking, pharmacophore modelling, molecular dynamic simulations, and ADMET analysis are elaborated. Overall, this review delves into the exploration of individual case studies to offer a wide perspective of the ever-evolving field of drug repurposing, emphasizing the multifaceted approaches and methodologies employed for the same to confront female cancers.

Bioinformatics Approaches in the Development of Antifungal Therapeutics and Vaccines

Fungal infections are considered a great threat to human life and are associated with high mortality and morbidity, especially in immunocompromised individuals. Fungal pathogens employ various defense mechanisms to evade the host immune system, which causes severe infections. The available repertoire of drugs for the treatment of fungal infections includes azoles, allylamines, polyenes, echinocandins, and antimetabolites. However, the development of multidrug and pandrug resistance to available antimycotic drugs increases the need to develop better treatment approaches. In this new era of -omics, bioinformatics has expanded options for treating fungal infections. This review emphasizes how bioinformatics complements the emerging strategies, including advancements in drug delivery systems, combination therapies, drug repurposing, epitope-based vaccine design, RNA-based therapeutics, and the role of gut-microbiome interactions to combat anti-fungal resistance. In particular, we focused on computational methods that can be useful to obtain potent hits, and that too in a short period.

Prolymphocytic Leukaemia: an Update on Biology and Treatment

PURPOSE OF REVIEW

This review summarises the recent advances in knowledge regarding the biology and treatment of prolymphocytic leukaemias.

RECENT FINDINGS

Both B-PLL and T-PLL are genetically complex, and the molecular landscape of these diseases has been well characterised recently. Diagnostic criteria for T-PLL have been refined with the publication of the first international consensus criteria, whereas the diagnosis of B-PLL has been thrown into question by the most recent WHO classification. Treatment advances in B-PLL have relied heavily on the advances seen in CLL that have then been extrapolated to B-PLL with just a few case reports to support the use of these targeted inhibitors. Despite increased knowledge of the biology of T-PLL and some elegant pre-clinical models to identify potential treatments, unfortunately, no improvements have been made in the treatment of T-PLL. Unmet need is a term oft used for many diseases, but this is particularly true for patients with prolymphocytic leukaemias. Ongoing improvements in our understanding of these diseases will hopefully lead to improved therapies in the future.

Escin-sorafenib synergy up-regulates LC3-II and p62 to induce apoptosis in hepatocellular carcinoma cells

INTRODUCTION

Hepatocellular carcinoma (HCC) is a common solid cancer and the leading cause of cancer deaths worldwide. Sorafenib is the first drug used to treat HCC but its effectiveness needs to be improved, and it is important to find ways to treat cancer that combine sorafenib with other drugs. Synergistic therapies lower effective drug doses and side effects while enhancing the anticancer effect.

PURPOSE

In the present study, the therapeutic potential of sorafenib in combination with escin and its underlying mechanism in targeting liver cancer has been established.

STUDY DESIGN/METHODS

The IC50 of sorafenib and escin against HepG2, PLC/PRF5 and Huh7 cell lines were determined using MTT assay. The combination index, dose reduction index, isobologram and concentrations producing synergy were evaluated using the Chou-Talaly algorithm. The sub-effective concentration of sorafenib and escin was selected to analyze cytotoxic synergistic potential. Cellular ROS, mitochondrial membrane potential, annexin V and cell cycle were evaluated using a flow-cytometer, and autophagy biomarkers were determined using western blotting. Moreover, autophagy was knocked down using ATG5 siRNA to confirm its role. A DEN-induced liver cancer rat model was developed to check the synergy of sorafenib and escin.

RESULTS

Different concentrations of escin reduced the IC50 of sorafenib in HepG2, PLC/PRF5 and Huh7 cell lines. Chou-Talaly algorithm determined cytotoxic synergistic concentrations of sorafenib and escin in these cell lines. Mechanistically, this combination over-expressed p62 and LC-II, reflecting autophagy block and induced late apoptosis, further reconfirmed by ATG5 knockdown. Sorafenib and escin combination  reduced HCC serum biomarker α-feto protein (α-FP) by 1.5 folds. This combination restricted liver weight, tumor number and size, also, conserved morphological features of liver cells. The combination selectively targeted the G0 /G1 phase of cancer cells.

CONCLUSION

Escin and sorafenib combination potentially up-regulates p62 to block autophagy to induce late apoptosis in liver cancer cells.

Mapping combinatorial drug effects to DNA damage response kinase inhibitors

One fundamental principle that underlies various cancer treatments, such as traditional chemotherapy and radiotherapy, involves the induction of catastrophic DNA damage, leading to the apoptosis of cancer cells. In our study, we conduct a comprehensive dose-response combination screening focused on inhibitors that target key kinases involved in the DNA damage response (DDR): ATR, ATM, and DNA-PK. This screening involves 87 anti-cancer agents, including six DDR inhibitors, and encompasses 62 different cell lines spanning 12 types of tumors, resulting in a total of 17,912 combination treatment experiments. Within these combinations, we analyze the most effective and synergistic drug pairs across all tested cell lines, considering the variations among cancers originating from different tissues. Our analysis reveals inhibitors of five DDR-related pathways (DNA topoisomerase, PLK1 kinase, p53-inducible ribonucleotide reductase, PARP, and cell cycle checkpoint proteins) that exhibit strong combinatorial efficacy and synergy when used alongside ATM/ATR/DNA-PK inhibitors.

Gossypol synergises antiproliferative effect of sorafenib in metastatic lung cancer cells following Chou-Talalay algorithm

Combination therapy has been proposed as a promising approach for lung cancer treatment, as it can enhance anticancer efficacy, and reduce dosages and adverse effects. This study aimed to explore the therapeutic potential of gossypol, a natural polyphenolic compound with sorafenib for treating lung cancer cells and elucidating its mechanism of action. The MTT assay was utilized to determine the IC50 of sorafenib and gossypol against A549 and NCI H460 cell lines. The Chou-Talaly algorithm was employed to determine the combination index (CI). A sub-effective concentration of sorafenib and gossypol was chosen to investigate the possibility of cytotoxic synergy. Autophagy biomarkers were identified using Western blotting, and the function of autophagy was determined using ATG5 siRNA. Results show that IC50 of sorafenib significantly reduced in A549 and NCI H460 cells when co-treated with gossypol. The combination treatment showed a synergistic cytotoxic effect against tested cell lines. The Chou-Talaly algorithm confirmed sorafenib's dose reduction index (DRI) up to 3.86. In A549 cells, combination treatment down-regulated p62 and up-regulated LC3-II, indicating the initiation of autophagy-dependent cytotoxicity. This was further confirmed by siRNA ATG5 knockdown. Additionally, the combination treatment exclusively targeted G0/G1 phase cancer cells. In conclusion, the combination of gossypol and sorafenib shows a synergistic increase in the cytotoxic effect by promoting autophagy and apoptosis.

Antifibrotic therapy in nonalcoholic steatohepatitis: time for a human-centric approach

Nonalcoholic steatohepatitis (NASH) might soon become the leading cause of end-stage liver disease and indication for liver transplantation worldwide. Fibrosis severity is the only histological predictor of liver-related morbidity and mortality in NASH identified to date. Moreover, fibrosis regression is associated with improved clinical outcomes. However, despite numerous clinical trials of plausible drug candidates, an approved antifibrotic therapy remains elusive. Increased understanding of NASH susceptibility and pathogenesis, emerging human multiomics profiling, integration of electronic health record data and modern pharmacology techniques hold enormous promise in delivering a paradigm shift in antifibrotic drug development in NASH. There is a strong rationale for drug combinations to boost efficacy, and precision medicine strategies targeting key genetic modifiers of NASH are emerging. In this Perspective, we discuss why antifibrotic effects observed in NASH pharmacotherapy trials have been underwhelming and outline potential approaches to improve the likelihood of future clinical success.

Escin enhanced the efficacy of sorafenib by autophagy-mediated apoptosis in lung cancer cells

Combining anti-cancer drugs has been exploited as promising treatment strategy to target lung cancer. Synergistic chemotherapies increase anti-cancer effect and reduce effective drug doses and side effects. In this study, therapeutic potential of escin in combination with sorafenib has been explored. 3-(4,5-Dimethylthiazol-2-yl)-2 5-diphenyltetrazolium bromide assay was used to calculate IC50 values. The synergy was evaluated using Chou-Talaly algorithm. Cellular reactive oxygen species, mitochondrial membrane potential, annexin V, and cell-cycle studies were done by flow-cytometer, and autophagy biomarkers expression were determined using western blotting. Moreover, autophagy was knocked down using ATG5 siRNA to confirm its role, diethylnitrosamine-induced lung cancer model was used to check the synergy of sorafenib/escin. Escin significantly reduced the IC50 of sorafenib in A549 and NCIH460 cells. The combination of sorafenib/escin produced a 2.95 and 5.45 dose reduction index for sorafenib in A549 and NCI-H460 cells. The combination of over-expressed p62 and LC3-II reflects autophagy block-mediated late apoptosis. This phenomenon was reconfirmed by ATG5 knockdown. This combination also selectively targeted G0/G1 phase of cancer cells. In in vivo study, the combination reduced tumour load and lower elevated serum biochemical parameters. The combination of sorafenib/escin synergistically inhibits autophagy to induce late apoptosis in lung cancer cells' G0/G1 phase.

Space Station-like Composite Nanoparticles for Co-Delivery of Multiple Natural Compounds from Chinese Medicine and Hydrogen in Combating Sensorineural Hearing Loss

Ototoxic drugs such as aminoglycoside antibiotics and cisplatin (CDDP) can cause sensorineural hearing loss (SNHL), which is closely related to oxidative stress and the acidification of the inner ear microenvironment. Effective treatment of SNHL often requires multifaceted approach due to the complex pathology, and drug combination therapy is expected to be at the forefront of modern hearing loss treatment. Here, space-station-like composite nanoparticles (CCC@mPP NPs) with pH/oxidation dual responsiveness and multidrug simultaneous delivery capability were constructed and then loaded with various drugs including panax notoginseng saponins (PNS), tanshinone IIA (TSIIA), and ammonia borane (AB) to provide robust protection against SNHL. Molecular dynamics simulation revealed that carboxymethyl chitosan/calcium carbonate-chitosan (CCC) NPs and monomethoxy poly(ethylene glycol)-PLGA (mPP) NPs can rendezvous and dock primarily by hydrogen bonding, and electrostatic forces may be involved. Moreover, CCC@mPP NPs crossed the round window membrane (RWM) and entered the inner ear through endocytosis and paracellular pathway. The docking state was basically maintained during this process, which created favorable conditions for multidrug delivery. This nanosystem was highly sensitive to pH and reactive oxygen species (ROS) changes, as evidenced by the restricted release of payload at alkaline condition (pH 7.4) without ROS, while significantly promoting the release in acidic condition (pH 5.0 and 6.0) with ROS. TSIIA/PNS/AB-loaded CCC@mPP NPs almost completely preserved the hair cells and remained the hearing threshold shift within normal limits in aminoglycoside- or CDDP-treated guinea pigs. Further experiments demonstrated that the protective mechanisms of TSIIA/PNS/AB-loaded CCC@mPP NPs involved direct and indirect scavenging of excessive ROS, and reduced release of pro-inflammatory cytokines. Both in vitro and in vivo experiments showed the high biocompatibility of the composite NPs, even after long-term administration. Collectively, this work suggests that composite NPs is an ideal multi-drug-delivery vehicle and open new avenues for inner ear disease therapies.

Erythroid/megakaryocytic differentiation confers BCL-XL dependency and venetoclax resistance in acute myeloid leukemia

Myeloid neoplasms with erythroid or megakaryocytic differentiation include pure erythroid leukemia, myelodysplastic syndrome with erythroid features, and acute megakaryoblastic leukemia (FAB M7) and are characterized by poor prognosis and limited treatment options. Here, we investigate the drug sensitivity landscape of these rare malignancies. We show that acute myeloid leukemia (AML) cells with erythroid or megakaryocytic differentiation depend on the antiapoptotic protein B-cell lymphoma (BCL)-XL, rather than BCL-2, using combined ex vivo drug sensitivity testing, genetic perturbation, and transcriptomic profiling. High-throughput screening of >500 compounds identified the BCL-XL-selective inhibitor A-1331852 and navitoclax as highly effective against erythroid/megakaryoblastic leukemia cell lines. In contrast, these AML subtypes were resistant to the BCL-2 inhibitor venetoclax, which is used clinically in the treatment of AML. Consistently, genome-scale CRISPR-Cas9 and RNAi screening data demonstrated the striking essentiality of BCL-XL-encoding BCL2L1 but not BCL2 or MCL1, for the survival of erythroid/megakaryoblastic leukemia cell lines. Single-cell and bulk transcriptomics of patient samples with erythroid and megakaryoblastic leukemias identified high BCL2L1 expression compared with other subtypes of AML and other hematological malignancies, where BCL2 and MCL1 were more prominent. BCL-XL inhibition effectively killed blasts in samples from patients with AML with erythroid or megakaryocytic differentiation ex vivo and reduced tumor burden in a mouse erythroleukemia xenograft model. Combining the BCL-XL inhibitor with the JAK inhibitor ruxolitinib showed synergistic and durable responses in cell lines. Our results suggest targeting BCL-XL as a potential therapy option in erythroid/megakaryoblastic leukemias and highlight an AML subgroup with potentially reduced sensitivity to venetoclax-based treatments.

Glioblastoma and the search for non-hypothesis driven combination therapeutics in academia

Glioblastoma (GBM) remains a cancer of high unmet clinical need. Current standard of care for GBM, consisting of maximal surgical resection, followed by ionisation radiation (IR) plus concomitant and adjuvant temozolomide (TMZ), provides less than 15-month survival benefit. Efforts by conventional drug discovery to improve overall survival have failed to overcome challenges presented by inherent tumor heterogeneity, therapeutic resistance attributed to GBM stem cells, and tumor niches supporting self-renewal. In this review we describe the steps academic researchers are taking to address these limitations in high throughput screening programs to identify novel GBM combinatorial targets. We detail how they are implementing more physiologically relevant phenotypic assays which better recapitulate key areas of disease biology coupled with more focussed libraries of small compounds, such as drug repurposing, target discovery, pharmacologically active and novel, more comprehensive anti-cancer target-annotated compound libraries. Herein, we discuss the rationale for current GBM combination trials and the need for more systematic and transparent strategies for identification, validation and prioritisation of combinations that lead to clinical trials. Finally, we make specific recommendations to the preclinical, small compound screening paradigm that could increase the likelihood of identifying tractable, combinatorial, small molecule inhibitors and better drug targets specific to GBM.

A comprehensive tool for tumor precision medicine with pharmaco-omics data analysis

Background: Cancer precision medicine is an effective strategy to fight cancers by bridging genomics and drug discovery to provide specific treatment for patients with different genetic characteristics. Although some public databases and modelling frameworks have been developed through studies on drug response, most of them only considered the ramifications of the drug on the cell line and the effects on the patient still require a huge amount of work to integrate data from various databases and calculations, especially concerning precision treatment. Furthermore, not only efficacy but also the adverse effects of drugs on patients should be taken into account during cancer treatment. However, the adverse effects as essential indicators of drug safety assessment are always neglected. Method: A holistic estimation explores various drugs' efficacy levels by calculating their potency both in reversing and enhancing cancer-associated gene expression change. And a method for bridging the gap between cell culture and living tissue estimates the effectiveness of a drug on individual patients through the mappings of various cell lines to each person according to their genetic mutation similarities. Result: We predicted the efficacy of FDA-recommended drugs, taking into account both efficacy and toxicity, and obtained consistent results. We also provided an intuitive and easy-to-use web server called DBPOM (http://www.dbpom.net/, a comprehensive database of pharmaco-omics for cancer precision medicine), which not only integrates the above methods but also provides calculation results on more than 10,000 small molecule compounds and drugs. As a one-stop web server, clinicians and drug researchers can also analyze the overall effect of a drug or a drug combination on cancer patients as well as the biological functions that they target. DBPOM is now public, free to use with no login requirement, and contains all the data and code. Conclusion: Both the positive and negative effects of drugs during precision treatment are essential for practical application of drugs. DBPOM based on the two effects will become a vital resource and analysis platform for drug development, drug mechanism studies and the discovery of new therapies.

Combinations of Host- and Virus-Targeting Antiviral Drugs Confer Synergistic Suppression of SARS-CoV-2

Three directly acting antivirals (DAAs) demonstrated substantial reduction in COVID-19 hospitalizations and deaths in clinical trials. However, these agents did not completely prevent severe illness and are associated with cases of rebound illness and viral shedding. Combination regimens can enhance antiviral potency, reduce the emergence of drug-resistant variants, and lower the dose of each component in the combination. Concurrently targeting virus entry and virus replication offers opportunities to discover synergistic drug combinations. While combination antiviral drug treatments are standard for chronic RNA virus infections, no antiviral combination therapy has been approved for SARS-CoV-2. Here, we demonstrate that combining host-targeting antivirals (HTAs) that target TMPRSS2 and hence SARS-CoV-2 entry, with the DAA molnupiravir, which targets SARS-CoV-2 replication, synergistically suppresses SARS-CoV-2 infection in Calu-3 lung epithelial cells. Strong synergy was observed when molnupiravir, an oral drug, was combined with three TMPRSS2 (HTA) oral or inhaled inhibitors: camostat, avoralstat, or nafamostat. The combination of camostat plus molnupiravir was also effective against the beta and delta variants of concern. The pyrimidine biosynthesis inhibitor brequinar combined with molnupiravir also conferred robust synergistic inhibition. These HTA+DAA combinations had similar potency to the synergistic all-DAA combination of molnupiravir plus nirmatrelvir, the protease inhibitor found in paxlovid. Pharmacodynamic modeling allowed estimates of antiviral potency at all possible concentrations of each agent within plausible therapeutic ranges, suggesting possible in vivo efficacy. The triple combination of camostat, brequinar, and molnupiravir further increased antiviral potency. These findings support the development of HTA+DAA combinations for pandemic response and preparedness. IMPORTANCE Imagine a future viral pandemic where if you test positive for the new virus, you can quickly take some medicines at home for a few days so that you do not get too sick. To date, only single drugs have been approved for outpatient use against SARS-CoV-2, and we are learning that these have some limitations and may succumb to drug resistance. Here, we show that combinations of two oral drugs are better than the single ones in blocking SARS-CoV-2, and we use mathematical modeling to show that these drug combinations are likely to work in people. We also show that a combination of three oral drugs works even better at eradicating the virus. Our findings therefore bode well for the development of oral drug cocktails for at home use at the first sign of an infection by a coronavirus or other emerging viral pathogens.

High Efficacy and Drug Synergy of HDAC6-Selective Inhibitor NN-429 in Natural Killer (NK)/T-Cell Lymphoma

NK/T-cell lymphoma (NKTCL) and γδ T-cell non-Hodgkin lymphomas (γδ T-NHL) are highly aggressive lymphomas that lack rationally designed therapies and rely on repurposed chemotherapeutics from other hematological cancers. Histone deacetylases (HDACs) have been targeted in a range of malignancies, including T-cell lymphomas. This study represents exploratory findings of HDAC6 inhibition in NKTCL and γδ T-NHL through a second-generation inhibitor NN-429. With nanomolar in vitro HDAC6 potency and high in vitro and in cellulo selectivity for HDAC6, NN-429 also exhibited long residence time and improved pharmacokinetic properties in contrast to older generation inhibitors. Following unique selective cytotoxicity towards γδ T-NHL and NKTCL, NN-429 demonstrated a synergistic relationship with the clinical agent etoposide and potential synergies with doxorubicin, cytarabine, and SNS-032 in these disease models, opening an avenue for combination treatment strategies.

Computational models, databases and tools for antibiotic combinations

Antibiotic combination is a promising strategy to extend the lifetime of antibiotics and thereby combat antimicrobial resistance. However, screening for new antibiotic combinations is both time-consuming and labor-intensive. In recent years, an increasing number of researchers have used computational models to predict effective antibiotic combinations. In this review, we summarized existing computational models for antibiotic combinations and discussed the limitations and challenges of these models in detail. In addition, we also collected and summarized available data resources and tools for antibiotic combinations. This study aims to help computational biologists design more accurate and interpretable computational models.

A new approach used in docking study for predicting the combination drug efficacy in EML4-ALK target of NSCLC

Combination drug treatments are usually used in many diseases, including cancers and AIDS. This treatment strategy is known as one of the cornerstone in therapies, which potentially reduces drug toxicity and drug resistance and also enhances therapeutic efficacy. Before using a drug in treatment, several experimental studies are done in vivo and in vitro to ensure the drug's efficacy. In such experimental studies, the drug's efficacy is evaluated with the help of drug dose ratio. In the combination drug experimental studies, the efficacy of the drugs is quantified with the Combination Index (CI) value and then interpreted by various terminologies like synergy, additive, and antagonism. Several computational models have now been invented for the speedy identification of combination drug efficacy. Unfortunately, none of these models have predicted the atomic level interaction of the combination drug with the target protein. This type of intermolecular interaction can be identified with the help of docking software. In the proposed work, we try to identify the intermolecular interaction and efficacy of the combination drug Crzizotinib and Temozolomide in the target of EML4-ALK in NSCLC by in silico study. The result of the study was evaluated with drug properties and Complex Energy (CE) of the docked complex rather than using docking score and binding energy. From this study, we could understand that first, Crizotinib and then after the Temozolomide drug binded on the EML4-ALK protein complex, showed very least CE and also identified that the combination of Crizotinib and Temozolomide drug are more effective in NSCLC.Communicated by Ramaswamy H. Sarma.

SynergyFinder 3.0: an interactive analysis and consensus interpretation of multi-drug synergies across multiple samples

SynergyFinder (https://synergyfinder.fimm.fi) is a free web-application for interactive analysis and visualization of multi-drug combination response data. Since its first release in 2017, SynergyFinder has become a popular tool for multi-dose combination data analytics, partly because the development of its functionality and graphical interface has been driven by a diverse user community, including both chemical biologists and computational scientists. Here, we describe the latest upgrade of this community-effort, SynergyFinder release 3.0, introducing a number of novel features that support interactive multi-sample analysis of combination synergy, a novel consensus synergy score that combines multiple synergy scoring models, and an improved outlier detection functionality that eliminates false positive results, along with many other post-analysis options such as weighting of synergy by drug concentrations and distinguishing between different modes of synergy (potency and efficacy). Based on user requests, several additional improvements were also implemented, including new data visualizations and export options for multi-drug combinations. With these improvements, SynergyFinder 3.0 supports robust identification of consistent combinatorial synergies for multi-drug combinatorial discovery and clinical translation.

Independent Drug Action in Combination Therapy: Implications for Precision Oncology

Combination therapies are superior to monotherapy for many cancers. This advantage was historically ascribed to the ability of combinations to address tumor heterogeneity, but synergistic interaction is now a common explanation as well as a design criterion for new combinations. We review evidence that independent drug action, described in 1961, explains the efficacy of many practice-changing combination therapies: it provides populations of patients with heterogeneous drug sensitivities multiple chances of benefit from at least one drug. Understanding response heterogeneity could reveal predictive or pharmacodynamic biomarkers for more precise use of existing drugs and realize the benefits of additivity or synergy.

Computational Pipeline for Rational Drug Combination Screening in Patient-Derived Cells

In many complex diseases, such as cancers, resistance to monotherapies easily occurs, and longer-term treatment responses often require combinatorial therapies as next-line regimens. However, due to a massive number of possible drug combinations to test, there is a need for systematic and rational approaches to finding safe and effective drug combinations for each individual patient. This protocol describes an ecosystem of computational methods to guide high-throughput combinatorial screening that help experimental researchers to identify optimal drug combinations in terms of synergy, efficacy, and/or selectivity for further preclinical and clinical investigation. The methods are demonstrated in the context of combinatorial screening in primary cells of leukemia patients, where the translational aim is to identify drug combinations that show not only high synergy but also maximal cancer-selectivity. The mechanism-agnostic and cost-effective computational methods are widely applicable to various cancer types, which are amenable to drug testing, as the computational methods take as input only the phenotypic measurements of a subset of drug combinations, without requiring target information or genomic profiles of the patient samples.

Drug Combinations as a First Line of Defense against Coronaviruses and Other Emerging Viruses

The world was unprepared for coronavirus disease 2019 (COVID-19) and remains ill-equipped for future pandemics. While unprecedented strides have been made developing vaccines and treatments for COVID-19, there remains a need for highly effective and widely available regimens for ambulatory use for novel coronaviruses and other viral pathogens. We posit that a priority is to develop pan-family drug cocktails to enhance potency, limit toxicity, and avoid drug resistance. We urge cocktail development for all viruses with pandemic potential both in the short term (<1 to 2 years) and longer term with pairs of drugs in advanced clinical testing or repurposed agents approved for other indications. While significant efforts were launched against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), in vitro and in the clinic, many studies employed solo drugs and had disappointing results. Here, we review drug combination studies against SARS-CoV-2 and other viruses and introduce a model-driven approach to assess drug pairs with the highest likelihood of clinical efficacy. Where component agents lack sufficient potency, we advocate for synergistic combinations to achieve therapeutic levels. We also discuss issues that stymied therapeutic progress against COVID-19, including testing of agents with low likelihood of efficacy late in clinical disease and lack of focus on developing virologic surrogate endpoints. There is a need to expedite efficient clinical trials testing drug combinations that could be taken at home by recently infected individuals and exposed contacts as early as possible during the next pandemic, whether caused by a coronavirus or another viral pathogen. The approach herein represents a proactive plan for global viral pandemic preparedness.

Leveraging laboratory and clinical studies to design effective antibiotic combination therapy

Interest in antibiotic combination therapy is increasing due to antimicrobial resistance and a slowing antibiotic pipeline. However, aside from specific indications, combination therapy in the clinic is often not administered systematically; instead, it is used at the physician's discretion as a bet-hedging mechanism to increase the chances of appropriately targeting a pathogen(s) with an unknown antibiotic resistance profile. Some recent clinical trials have been unable to demonstrate superior efficacy of combination therapy over monotherapy. Other trials have shown a benefit of combination therapy in defined circumstances consistent with recent studies indicating that factors including species, strain, resistance profile, and microenvironment affect drug combination efficacy and drug interactions. In this review, we discuss how a careful study design that takes these factors into account, along with the different drug interaction and potency metrics for assessing combination performance, may provide the necessary insight to understand the best clinical use-cases for combination therapy.

iTReX: Interactive exploration of mono- and combination therapy dose response profiling data

High throughput screening methods, measuring the sensitivity and resistance of tumor cells to drug treatments have been rapidly evolving. Not only do these screens allow correlating response profiles to tumor genomic features for developing novel predictors of treatment response, but they can also add evidence for therapy decision making in precision oncology. Recent analysis methods developed for either assessing single agents or combination drug efficacies enable quantification of dose-response curves with restricted symmetric fit settings. Here, we introduce iTReX, a user-friendly and interactive Shiny/R application, for both the analysis of mono- and combination therapy responses. The application features an extended version of the drug sensitivity score (DSS) based on the integral of an advanced five-parameter dose-response curve model and a differential DSS for combination therapy profiling. Additionally, iTReX includes modules that visualize drug target interaction networks and support the detection of matches between top therapy hits and the sample omics features to enable the identification of druggable targets and biomarkers. iTReX enables the analysis of various quantitative drug or therapy response readouts (e.g. luminescence, fluorescence microscopy) and multiple treatment strategies (drug treatments, radiation). Using iTReX we validate a cost-effective drug combination screening approach and reveal the application's ability to identify potential sample-specific biomarkers based on drug target interaction networks. The iTReX web application is accessible at https://itrex.kitz-heidelberg.de.

High-throughput screening for drug discovery targeting the cancer cell-microenvironment interactions in hematological cancers

INTRODUCTION

The interactions between leukemic blasts and cells within the bone marrow environment affect oncogenesis, cancer stem cell survival, as well as drug resistance in hematological cancers. The importance of this interaction is increasingly being recognized as a potentially important target for future drug discoveries and developments. Recent innovations in the high throughput drug screening-related technologies, novel ex-vivo disease-models, and freely available machine-learning algorithms are advancing the drug discovery process by targeting earlier undruggable proteins, complex pathways, as well as physical interactions (e.g. leukemic cell-bone microenvironment interaction).

AREA COVERED

In this review, the authors discuss the recent methodological advancements and existing challenges to target specialized hematopoietic niches within the bone marrow during leukemia and suggest how such methods can be used to identify drugs targeting leukemic cell-bone microenvironment interactions.

EXPERT OPINION

The recent development in cell-cell communication scoring technology and culture conditions can speed up the drug discovery by targeting the cell-microenvironment interaction. However, to accelerate this process, collecting clinical-relevant patient tissues, developing culture model systems, and implementing computational algorithms, especially trained to predict drugs and their combination targeting the cancer cell-bone microenvironment interaction are needed.

Machine learning approaches for drug combination therapies

Drug combination therapy is a promising strategy to treat complex diseases such as cancer and infectious diseases. However, current knowledge of drug combination therapies, especially in cancer patients, is limited because of adverse drug effects, toxicity and cell line heterogeneity. Screening new drug combinations requires substantial efforts since considering all possible combinations between drugs is infeasible and expensive. Therefore, building computational approaches, particularly machine learning methods, could provide an effective strategy to overcome drug resistance and improve therapeutic efficacy. In this review, we group the state-of-the-art machine learning approaches to analyze personalized drug combination therapies into three categories and discuss each method in each category. We also present a short description of relevant databases used as a benchmark in drug combination therapies and provide a list of well-known, publicly available interactive data analysis portals. We highlight the importance of data integration on the identification of drug combinations. Finally, we address the advantages of combining multiple data sources on drug combination analysis by showing an experimental comparison.

The optimization of combinatorial drug therapies: Strategies and laboratorial platforms

Designing optimal combinatorial drug therapies is challenging, because the drug interactions depend not only on the drugs involved, but also on their doses. With recent advances, combinatorial drug therapy is closer than ever to clinical application. Herein, we summarize approaches and advances over the past decade for identifying and optimizing drug combination therapies, with innovations across research fields, covering physical laboratory platforms for combination screening to computational models and algorithms designed for synergism prediction and optimization. By comparing different types of approach, we detail a three-step workflow that could maximize the overall optimization efficiency, thus enabling the application of personalized optimization of combinatorial drug therapy.

Development of HDAC Inhibitors Exhibiting Therapeutic Potential in T-Cell Prolymphocytic Leukemia

Epigenetic targeting has emerged as an efficacious therapy for hematological cancers. The rare and incurable T-cell prolymphocytic leukemia (T-PLL) is known for its aggressive clinical course. Current epigenetic agents such as histone deacetylase (HDAC) inhibitors are increasingly used for targeted therapy. Through a structure-activity relationship (SAR) study, we developed an HDAC6 inhibitor KT-531, which exhibited higher potency in T-PLL compared to other hematological cancers. KT-531 displayed strong HDAC6 inhibitory potency and selectivity, on-target biological activity, and a safe therapeutic window in nontransformed cell lines. In primary T-PLL patient cells, where HDAC6 was found to be overexpressed, KT-531 exhibited strong biological responses, and safety in healthy donor samples. Notably, combination studies in T-PLL patient samples demonstrated KT-531 synergizes with approved cancer drugs, bendamustine, idasanutlin, and venetoclax. Our work suggests HDAC inhibition in T-PLL could afford sufficient therapeutic windows to achieve durable remission either as stand-alone or in combination with targeted drugs.

Recent advances in MOF-based nanoplatforms generating reactive species for chemodynamic therapy

Still today, cancer remains a threat to human health. Possible common treatments to cure this disease include chemotherapy (CT), radiotherapy (RT), photothermal therapy (PTT), and surgical resection, which give unreasonable results because of their limited efficiency and also lead to side-effects. Hence, different strategies are now being exploited to not only enhance the efficiency of these traditional therapeutic methods or treat the tumor cells but also curtail the side effects. A latest method with authentic proof of chemodynamic therapy (CDT) utilizing the Fenton reaction is now gaining importance. This approach, which is developed based on the high level of hydrogen peroxide (H2O2) in a tumor microenvironment (TME), can be used to catalyze the Fenton reaction to generate cancer cell-killing reactive oxygen species (ROS). The selection of materials is extremely important and nanomaterials offer the most likely method to facilitate CDT. Among various materials, metal-organic frameworks (MOFs) which have been extensively applied in medical areas are regarded as a promising material and possess potential for the next generation of nanotechnology. This review focuses on summarizing the use of MOFs in CDT and their synergetic therapeutics as well as the challenges, obstacles, and development.

Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy

Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others, leads to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine-learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine-learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction, and analysis of virus-host interactions. Our review also covers the perspectives of using the machine-learning approaches for antiviral research, including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses, and some others.

Re-defining synthetic lethality by phenotypic profiling for precision oncology

High-throughput functional and genomic screening techniques provide systematic means for phenotypic discovery. Using synthetic lethality (SL) as a paradigm for anticancer drug and target discovery, we describe how these screening technologies may offer new possibilities to identify therapeutically relevant and selective SL interactions by addressing some of the challenges that have made robust discovery of SL candidates difficult. We further introduce an extended concept of SL interaction, in which a simultaneous perturbation of two or more cellular components reduces cell viability more than expected by their individual effects, which we feel is highly befitting for anticancer applications. We also highlight the potential benefits and challenges related to computational quantification of synergistic interactions and cancer selectivity. Finally, we explore how tumoral heterogeneity can be exploited to find phenotype-specific SL interactions for precision oncology using high-throughput functional screening and the exciting opportunities these methods provide for the identification of subclonal SL interactions.

Patient-tailored design for selective co-inhibition of leukemic cell subpopulations

The extensive drug resistance requires rational approaches to design personalized combinatorial treatments that exploit patient-specific therapeutic vulnerabilities to selectively target disease-driving cell subpopulations. To solve the combinatorial explosion challenge, we implemented an effective machine learning approach that prioritizes patient-customized drug combinations with a desired synergy-efficacy-toxicity balance by combining single-cell RNA sequencing with ex vivo single-agent testing in scarce patient-derived primary cells. When applied to two diagnostic and two refractory acute myeloid leukemia (AML) patient cases, each with a different genetic background, we accurately predicted patient-specific combinations that not only resulted in synergistic cancer cell co-inhibition but also were capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Our functional precision oncology approach provides an unbiased means for systematic identification of personalized combinatorial regimens that selectively co-inhibit leukemic cells while avoiding inhibition of nonmalignant cells, thereby increasing their likelihood for clinical translation.

Repurposing Pyramax®, quinacrine and tilorone as treatments for Ebola virus disease

We have recently identified three molecules (tilorone, quinacrine and pyronaridine tetraphosphate) which all demonstrated efficacy in the mouse model of infection with mouse-adapted Ebola virus (EBOV) model of disease and had similar in vitro inhibition of an Ebola pseudovirus (VSV-EBOV-GP), suggesting they interfere with viral entry. Using a machine learning model to predict lysosomotropism these compounds were evaluated for their ability to possess a lysosomotropic mechanism in vitro. We now demonstrate in vitro that pyronaridine tetraphosphate is an inhibitor of Lysotracker accumulation in lysosomes (IC50 = 0.56 μM). Further, we evaluated antiviral synergy between pyronaridine and artesunate (Pyramax®), which are used in combination to treat malaria. Artesunate was not found to have lysosomotropic activity in vitro and the combination effect on EBOV inhibition was shown to be additive. Pyramax® may represent a unique example of the repurposing of a combination product for another disease.

SynergyFinder 2.0: visual analytics of multi-drug combination synergies

SynergyFinder (https://synergyfinder.fimm.fi) is a stand-alone web-application for interactive analysis and visualization of drug combination screening data. Since its first release in 2017, SynergyFinder has become a widely used web-tool both for the discovery of novel synergistic drug combinations in pre-clinical model systems (e.g. cell lines or primary patient-derived cells), and for better understanding of mechanisms of combination treatment efficacy or resistance. Here, we describe the latest version of SynergyFinder (release 2.0), which has extensively been upgraded through the addition of novel features supporting especially higher-order combination data analytics and exploratory visualization of multi-drug synergy patterns, along with automated outlier detection procedure, extended curve-fitting functionality and statistical analysis of replicate measurements. A number of additional improvements were also implemented based on the user requests, including new visualization and export options, updated user interface, as well as enhanced stability and performance of the web-tool. With these improvements, SynergyFinder 2.0 is expected to greatly extend its potential applications in various areas of multi-drug combinatorial screening and precision medicine.

Charting the Fragmented Landscape of Drug Synergy

Even as the clinical impact of drug combinations continues to accelerate, no consensus on how to quantify drug synergy has emerged. Rather, surveying the landscape of drug synergy reveals the persistence of historical fissures regarding the appropriate domains of conflicting synergy models - fissures impacting all aspects of combination therapy discovery and deployment. Herein we chronicle the impact of these divisions on: (i) the design, interpretation, and reproducibility of high-throughput combination screens; (ii) the performance of algorithms to predict synergistic mixtures; and (iii) the search for higher-order synergistic interactions. Further progress in each of these subfields hinges on reaching a consensus regarding the long-standing rifts in the field.