Ex Vivo Assessment of Drug Activity in Patient Tumor Cells as a Basis for Tailored Cancer Therapy

Fast acoustic droplet ejection based on annular array transducer

Acoustic droplet ejection (ADE) has become the preferred method for liquid transfer in a variety of applications including synthetic biology, genotyping and drug discovery. Comparing with traditional pipetting techniques, the accuracy and data reproducibility of ADE based liquid transfer are improved, waste and cost are reduced, and cross-contamination is eliminated. The key component in the ADE system is the ultrasound transducer, which is responsible for generating focused ultrasound beam for droplet ejection. However, current ADE systems commonly utilize a single-element focused transducer with a fixed focal length that require mechanical movement to focus on the liquid surface, resulting in reduced liquid transfer efficiency. In this study, we first present a high-frequency annular array transducer for the ADE technology, which enables rapid and dynamic axial focusing to the liquid surface without mechanically moving the transducer, thereby accelerating liquid transfer. Experimental results show that the proposed 10 MHz, 5-element annular array transducer has good dynamic axial focusing ability, and can achieve accurate and stable droplet ejection of nanoliter volume at the designed focal length of 26-32 mm. Our results highlight the potential of the annular array transducer in advancing ADE system for rapid liquid transfer. This technology is expected to be useful in a variety of applications where precise and high-throughput liquid transfer is crucial.

Exhaustive in vitro evaluation of the 9-drug cocktail CUSP9 for treatment of glioblastoma

The CUSP9 protocol is a polypharmaceutical strategy aiming at addressing the complexity of glioblastoma by targeting multiple pathways. Although the rationale for this 9-drug cocktail is well-supported by theoretical and in vitro data, its effectiveness compared to its 511 possible subsets has not been comprehensively evaluated. Such an analysis could reveal if fewer drugs could achieve similar or better outcomes. We conducted an exhaustive in vitro evaluation of the CUSP9 protocol using COMBImageDL, our specialized framework for testing higher-order drug combinations. This study assessed all 511 subsets of the CUSP9v3 protocol, in combination with temozolomide, on two clonal cultures of glioma-initiating cells derived from patient samples. The drugs were used at fixed, clinically relevant concentrations, and the experiment was performed in quadruplicate with endpoint cell viability and live-cell imaging readouts. Our results showed that several lower-order drug combinations produced effects equivalent to the full CUSP9 cocktail, indicating potential for simplified regimens in personalized therapy. Further validation through in vivo and precision medicine testing is required. Notably, a subset of four drugs (auranofin, disulfiram, itraconazole, sertraline) was particularly effective, reducing cell growth, altering cell morphology, increasing apoptotic-like cells within 4-28 h, and significantly decreasing cell viability after 68 h compared to untreated cells. This study underscores the importance and feasibility of comprehensive in vitro evaluations of complex drug combinations on patient-derived tumor cells, serving as a critical step toward (pre-)clinical development.

Comparison of high-throughput single-cell RNA-seq methods for ex vivo drug screening

Functional precision medicine (FPM) aims to optimize patient-specific drug selection based on the unique characteristics of their cancer cells. Recent advancements in high throughput ex vivo drug profiling have accelerated interest in FPM. Here, we present a proof-of-concept study for an integrated experimental system that incorporates ex vivo treatment response with a single-cell gene expression output enabling barcoding of several drug conditions in one single-cell sequencing experiment. We demonstrate this through a proof-of-concept investigation focusing on the glucocorticoid-resistant acute lymphoblastic leukemia (ALL) E/R+ Reh cell line. Three different single-cell transcriptome sequencing (scRNA-seq) approaches were evaluated, each exhibiting high cell recovery and accurate tagging of distinct drug conditions. Notably, our comprehensive analysis revealed variations in library complexity, sensitivity (gene detection), and differential gene expression detection across the methods. Despite these differences, we identified a substantial transcriptional response to fludarabine, a highly relevant drug for treating high-risk ALL, which was consistently recapitulated by all three methods. These findings highlight the potential of our integrated approach for studying drug responses at the single-cell level and emphasize the importance of method selection in scRNA-seq studies. Finally, our data encompassing 27 327 cells are freely available to extend to future scRNA-seq methodological comparisons.

Multimodal machine learning models identify chemotherapy drugs with prospective clinical efficacy in dogs with relapsed B-cell lymphoma

Dogs with B-cell lymphoma typically respond well to first-line CHOP-based chemotherapy, but there is no standard of care for relapsed patients. To help veterinary oncologists select effective drugs for dogs with lymphoid malignancies such as B-cell lymphoma, we have developed multimodal machine learning models that integrate data from multiple tumor profiling modalities and predict the likelihood of a positive clinical response for 10 commonly used chemotherapy drugs. Here we report on clinical outcomes that occurred after oncologists received a prediction report generated by our models. Remarkably, we found that dogs that received drugs predicted to be effective by the models experienced better clinical outcomes by every metric we analyzed (overall response rate, complete response rate, duration of complete response, patient survival times) relative to other dogs in the study and relative to historical controls.

Microphysiological Conditions Do Not Affect MDR1-Mediated Transport of Rhodamine 123 above an Artificial Proximal Tubule

Despite disadvantages, such as high cost and their poor predictive value, animal experiments are still the state of the art for pharmaceutical substance testing. One reason for this problem is the inability of standard cell culture methods to emulate the physiological environment necessary to recapitulate in vivo processes. Microphysiological systems offer the opportunity to close this gap. In this study, we utilize a previously employed microphysiological system to examine the impact of pressure and flow on the transportation of substances mediated by multidrug resistance protein 1 (MDR1) across an artificial cell-based tubular barrier. By using a miniaturized fluorescence measurement device, we could continuously track the MDR1-mediated transport of rhodamine 123 above the artificial barrier over 48 h. We proved that applying pressure and flow affects both active and passive transport of rhodamine 123. Using experimental results and curve fittings, the kinetics of MDR1-mediated transport as well as passive transport were investigated; thus, a kinetic model that explains this transport above an artificial tubular barrier was identified. This kinetic model demonstrates that the simple Michaelis-Menten model is not an appropriate model to explain the MDR1-mediated transport; instead, Hill kinetics, with Hill slope of n = 2, is a better fit. The kinetic values, K_m, V_max, and apparent permeability (P_app), obtained in this study are comparable with other in vivo and in vitro studies. Finally, the presented proximal tubule-on-a-chip can be used for pharmaceutical substance testing and to investigate pharmacokinetics of the renal transporter MDR1.

Ex vivo assessment of cancer drug sensitivity in epithelial ovarian cancer and its association with histopathological type, treatment history and clinical outcome

Epithelial ovarian cancer (EOC) is divided into type I and type II based on histopathological features. Type I is clinically more indolent, but also less sensitive to chemotherapy, compared with type II. The basis for this difference is not fully clarified. The present study investigated the pattern of drug activity in type I and type II EOC for standard cytotoxic drugs and recently introduced tyrosine kinase inhibitors (TKIs), and assessed the association with treatment history and clinical outcome. Isolated EOC tumor cells obtained at surgery were investigated for their sensitivity to seven standard cytotoxic drugs and nine TKIs using a short-term fluorescent microculture cytotoxicity assay (FMCA). Drug activity was compared with respect to EOC subtype, preoperative chemotherapy, cross-resistance and association with progression-free survival (PFS). Out of 128 EOC samples, 120 samples, including 21 type I and 99 type II, were successfully analyzed using FMCA. Patients with EOC type I had a significantly longer PFS time than patients with EOC type II (P=0.01). In line with clinical experience, EOC type I samples were generally more resistant than type II samples to both standard cytotoxic drugs and the TKIs, reaching statistical significance for cisplatin (P=0.03) and dasatinib (P=0.002). A similar pattern was noted in samples from patients treated with chemotherapy prior to surgery compared with treatment-naive samples, reaching statistical significance for fluorouracil, irinotecan, dasatinib and nintedanib (all P<0.05). PFS time gradually shortened with increasing degree of drug resistance. Cross-resistance between drugs was in most cases statistically significant yet moderate in degree (r<0.5). The clinically observed relative drug resistance of EOC type I, as well as in patients previously treated, is at least partly due to mechanisms in the tumor cells. These mechanisms seemingly also encompass kinase inhibitors. Ex vivo assessment of drug activity is suggested to have a role in the optimization of drug therapy in EOC.

Perillaldehyde is a new ferroptosis inducer with a relevant clinical potential for acute myeloid leukemia therapy

Ferroptosis induction is an emerging strategy to treat cancer and contrast the tricky issue of chemoresistance, which can arise towards apoptosis. This work elucidates the anticancer mechanisms evoked by perillaldehyde, a monoterpenoid isolated from Ammodaucus leucotrichus Coss. & Dur. We investigated and characterized its antileukemic potential in vitro, disclosing its ability to trigger ferroptosis. Specifically, perillaldehyde induced lipid peroxidation, decreased glutathione peroxidase 4 protein expression, and depleted intracellular glutathione on HL-60 promyelocytic leukemia cells. Besides, it stimulated the active secretion of ATP, one of the most crucial events in the induction of efficient anticancer response, prompting further studies to disclose its possible nature as an immunogenic cell death inducer. To preliminarily assess the clinical relevance of perillaldehyde, we tested its ability to induce cell death on patient-derived acute myeloid leukemia biopsies, recording a similar mechanism of action and potency compared to HL-60 cells. To round the study off, we tested its selectivity towards tumor cells and disclosed lower toxicity on normal cells compared to both HL-60 and acute myeloid leukemia biopsies. Altogether, these data depict a favorable risk-benefit profile for perillaldehyde and reveal its peculiar antileukemic potential, which qualifies this natural product to proceed further through the drug development pipeline.

Sorafenib and nitazoxanide disrupt mitochondrial function and inhibit regrowth capacity in three-dimensional models of hepatocellular and colorectal carcinoma

Quiescent cancer cells in malignant tumors can withstand cell-cycle active treatment and cause cancer spread and recurrence. Three-dimensional (3D) cancer cell models have led to the identification of oxidative phosphorylation (OXPHOS) as a context-dependent vulnerability. The limited treatment options for advanced hepatocellular carcinoma (HCC) and colorectal carcinoma (CRC) metastatic to the liver include the multikinase inhibitors sorafenib and regorafenib. Off-target effects of sorafenib and regorafenib are related to OXPHOS inhibition; however the importance of this feature to the effect on tumor cells has not been investigated in 3D models. We began by assessing global transcriptional responses in monolayer cell cultures, then moved on to multicellular tumor spheroids (MCTS) and tumoroids generated from a CRC patient. Cells were treated with chemotherapeutics, kinase inhibitors, and the OXPHOS inhibitors. Cells grown in 3D cultures were sensitive to the OXPHOS inhibitor nitazoxanide, sorafenib, and regorafenib and resistant to other multikinase inhibitors and chemotherapeutic drugs. Furthermore, nitazoxanide and sorafenib reduced viability, regrowth potential and inhibited mitochondrial membrane potential in an additive manner at clinically relevant concentrations. This study demonstrates that the OXPHOS inhibition caused by sorafenib and regorafenib parallels 3D activity and can be further investigated for new combination strategies.

A review on acoustic droplet ejection technology and system

The pace of change in chemical and biological research enabled by improved detection systems demands fundamental liquid handling and sample preparation changes. The acoustic droplet ejection (ADE)-based liquid handling method has the advantages of improving precision and data reproducibility, reducing costs, hands-on time, and eliminating waste. ADE gradually replaced traditional aspiration-and-dispense liquid-handling robots in applications such as synthetic biology, genotyping, personalized medicine, and next-generation sequencing. This review emphatically introduces the setup of the ADE system and the critical technologies of each part, including acoustic droplet generation, optimized design of the source fluid wells, droplet coalescence, and power control. The advantages and disadvantages of these technologies are discussed, and the future development of acoustic droplet ejection technology is also predicted.

Targeting aggressive osteosarcoma with a peptidase-enhanced cytotoxic melphalan flufenamide

Background:

Low survival rates in metastatic high-grade osteosarcoma (HGOS) have remained stagnant for the last three decades. This study aims to investigate the role of aminopeptidase N (ANPEP) in HGOS progression and its targeting with a novel lipophilic peptidase-enhanced cytotoxic compound melphalan flufenamide (melflufen) in HGOS.

Methods:

Meta-analysis of publicly available gene expression datasets was performed to determine the impact of ANPEP gene expression on metastasis-free survival of HGOS patients. The efficacy of standard-of-care anti-neoplastic drugs and a lipophilic peptidase-enhanced cytotoxic conjugate melflufen was investigated in patient-derived HGOS ex vivo models and cell lines. The kinetics of apoptosis and necrosis induced by melflufen and doxorubicin were compared. Anti-neoplastic effects of melflufen were investigated in vivo.

Results:

Elevated ANPEP expression in diagnostic biopsies of HGOS patients was found to significantly reduce metastasis-free survival. In drug sensitivity assays, melflufen has shown an anti-proliferative effect in HGOS ex vivo samples and cell lines, including those resistant to methotrexate, etoposide, doxorubicin, and PARP inhibitors. Further, HGOS cells treated with melflufen displayed a rapid induction of apoptosis and this sensitivity correlated with high expression of ANPEP. In combination treatments, melflufen demonstrated synergy with doxorubicin in killing HGOS cells. Finally, Melflufen displayed anti-tumor growth and anti-metastatic effects in vivo.

Conclusion:

This study may pave the way for use of melflufen as an adjuvant to doxorubicin in improving the therapeutic efficacy for the treatment of metastatic HGOS.

COMBImage2: a parallel computational framework for higher-order drug combination analysis that includes automated plate design, matched filter based object counting and temporal data mining

BACKGROUND

Pharmacological treatment of complex diseases using more than two drugs is commonplace in the clinic due to better efficacy, decreased toxicity and reduced risk for developing resistance. However, many of these higher-order treatments have not undergone any detailed preceding in vitro evaluation that could support their therapeutic potential and reveal disease related insights. Despite the increased medical need for discovery and development of higher-order drug combinations, very few reports from systematic large-scale studies along this direction exist. A major reason is lack of computational tools that enable automated design and analysis of exhaustive drug combination experiments, where all possible subsets among a panel of pre-selected drugs have to be evaluated.

RESULTS

Motivated by this, we developed COMBImage2, a parallel computational framework for higher-order drug combination analysis. COMBImage2 goes far beyond its predecessor COMBImage in many different ways. In particular, it offers automated 384-well plate design, as well as quality control that involves resampling statistics and inter-plate analyses. Moreover, it is equipped with a generic matched filter based object counting method that is currently designed for apoptotic-like cells. Furthermore, apart from higher-order synergy analyses, COMBImage2 introduces a novel data mining approach for identifying interesting temporal response patterns and disentangling higher- from lower- and single-drug effects. COMBImage2 was employed in the context of a small pilot study focused on the CUSP9v4 protocol, which is currently used in the clinic for treatment of recurrent glioblastoma. For the first time, all 246 possible combinations of order 4 or lower of the 9 single drugs consisting the CUSP9v4 cocktail, were evaluated on an in vitro clonal culture of glioma initiating cells.

CONCLUSIONS

COMBImage2 is able to automatically design and robustly analyze exhaustive and in general higher-order drug combination experiments. Such a versatile video microscopy oriented framework is likely to enable, guide and accelerate systematic large-scale drug combination studies not only for cancer but also other diseases.

Tumor Tissue Explant Culture of Patient-Derived Xenograft as Potential Prioritization Tool for Targeted Therapy

Despite of remarkable progress made in the head and neck cancer (HNC) therapy, the survival rate of this metastatic disease remain low. Tailoring the appropriate therapy to patients is a major challenge and highlights the unmet need to have a good preclinical model that will predict clinical response. Hence, we developed an accurate and time efficient drug screening method of tumor ex vivo analysis (TEVA) system, which can predict patient-specific drug responses. In this study, we generated six patient derived xenografts (PDXs) which were utilized for TEVA. Briefly, PDXs were cut into 2 × 2 × 2 mm3 explants and treated with clinically relevant drugs for 24 h. Tumor cell proliferation and death were evaluated by immunohistochemistry and TEVA score was calculated. Ex vivo and in vivo drug efficacy studies were performed on four PDXs and three drugs side-by-side to explore correlation between TEVA and PDX treatment in vivo. Efficacy of drug combinations was also ventured. Optimization of the culture timings dictated 24 h to be the time frame to detect drug responses and drug penetrates 2 × 2 × 2 mm3 explants as signaling pathways were significantly altered. Tumor responses to drugs in TEVA, significantly corresponds with the drug efficacy in mice. Overall, this low cost, robust, relatively simple and efficient 3D tissue-based method, employing material from one PDX, can bypass the necessity of drug validation in immune-incompetent PDX-bearing mice. Our data provides a potential rationale for utilizing TEVA to predict tumor response to targeted and chemo therapies when multiple targets are proposed.

Ex vivo activity of cytotoxic drugs and targeted agents in small intestinal neuroendocrine tumors

Small intestinal neuroendocrine tumors (SI-NETs) are generally considered resistant to systemic treatment. To date, predictive markers for drug activity are lacking. Tumor samples from 27 patients with SI-NETs were analyzed ex vivo for sensitivity to a panel of cytotoxic drugs and targeted agents using a short-term total cell kill assay. Samples of renal cancer, colorectal cancer (CRC), ovarian cancer and chronic lymphocytic leukemia (CLL) were included for comparison. For the SI-NET subset, drug sensitivity was analyzed in relation to clinicopathological variables and pre-treatment biomarkers. For cytotoxic drugs, SI-NETs demonstrated similar or higher sensitivity to 5-FU, platinum, gemcitabine and doxorubicin compared with CRC. For several of the targeted kinase inhibitors, SI-NET was among the most sensitive solid tumor types. CLL and ovarian cancer were generally the most sensitive tumor types to both cytotoxic drugs and protein kinase inhibitors. SI-NET was more sensitive to the mTOR inhibitor sirolimus than the other solid tumor types tested. Individual SI-NET samples demonstrated great variability in ex vivo sensitivity for most drugs. Cross-resistance between different drugs also varied considerably, being higher among protein kinase inhibitors. Age, stage, grade, peritoneal carcinomatosis and extra-abdominal metastases as well as serum chromogranin A and urine 5-HIAA concentrations at diagnosis did not correlate to drug sensitivity ex vivo SI-NETs exhibit intermediate sensitivity ex vivo to cytotoxic and targeted drugs. Clinicopathological factors and currently used biomarkers are not clearly associated to ex vivo sensitivity, challenging these criteria for treatment decisions in SI-NET. The great variability in drug sensitivity calls for individualized selection of therapy.

In vitro and in vivo anti-leukemic activity of the peptidase-potentiated alkylator melflufen in acute myeloid leukemia

The novel aminopeptidase potentiated alkylating agent melflufen, was evaluated for activity in acute myeloid leukemia in a range of in vitro models, as well as in a patient derived xenograft study. All tested AML cell lines were highly sensitive to melflufen while melphalan was considerably less potent. In the HL-60 cell line model, synergy was observed for the combination of melflufen and cytarabine, an interaction that appeared sequence dependent with increased synergy when melflufen was added before cytarabine. Also, in primary cultures of AML cells from patients melflufen was highly active, while normal PBMC cultures appeared less sensitive, indicating a 7-fold in vitro therapeutic index. Melphalan, on the other hand, was only 2-fold more potent in the AML patient samples compared with PBMCs. Melflufen was equally active against non-malignant, immature CD34⁺ progenitor cells and a more differentiated CD34⁺ derived cell population (GM14), whereas the stem cell like cells were less sensitive to melphalan. Finally, melflufen treatment showed significant anti-leukemia activity and increased survival in a patient derived xenograft of AML in mice. In conclusion, melflufen demonstrates high and significant preclinical activity in AML and further clinical evaluation seem warranted in this disease.

Preclinical activity of melflufen (J1) in ovarian cancer

Ovarian cancer carries a significant mortality. Since symptoms tend to be minimal, the disease is often diagnosed when peritoneal metastases are already present. The standard of care in advanced ovarian cancer consists of platinum-based chemotherapy combined with cytoreductive surgery. Unfortunately, even after optimal cytoreduction and adjuvant chemotherapy, most patients with stage III disease will develop a recurrence. Intraperitoneal administration of chemotherapy is an alternative treatment for patients with localized disease. The pharmacological and physiochemical properties of melflufen, a peptidase potentiated alkylator, raised the hypothesis that this drug could be useful in ovarian cancer and particularily against peritoneal carcinomatosis. In this study the preclinical effects of melflufen were investigated in different ovarian cancer models. Melflufen was active against ovarian cancer cell lines, primary cultures of patient-derived ovarian cancer cells, and inhibited the growth of subcutaneous A2780 ovarian cancer xenografts alone and when combined with gemcitabine or liposomal doxorubicin when administered intravenously. In addition, an intra- and subperitoneal xenograft model showed activity of intraperitoneal administered melflufen for peritoneal carcinomatosis, with minimal side effects and modest systemic exposure. In conclusion, results from this study support further investigations of melflufen for the treatment of peritoneal carcinomatosis from ovarian cancer, both for intravenous and intraperitoneal administration.

Prediction of drug cocktail effects when the number of measurements is limited

Cocktails of drugs can be more effective than single drugs, because they can potentially work at lower doses and avoid resistance. However, it is impossible to test all drug cocktails drawn from a large set of drugs because of the huge number of combinations. To overcome this combinatorial explosion problem, one can sample a relatively small number of combinations and use a model to predict the rest. Recently, Zimmer and Katzir et al. presented a model that accurately predicted the effects of cocktails at all doses based on measuring pairs of drugs. This model requires measuring each pair at several different doses and uses interpolation to reduce experimental noise. However, often, it is not possible to measure each pair at multiple doses (for example, in scarce patient-derived tumor material or in large screens). Here, we ask whether measurements at only a single dose can also predict high-order drug cocktails. To address this, we present a fully factorial experimental dataset on all drug cocktails built of 6 chemotherapy drugs on 2 cancer cell lines. We develop a formula that uses only pair measurements at a single dose to predict much of the variation up to 6-drug cocktails in the present data, outperforming commonly used Bliss independence and regression approaches. This model, called the pairs model, is an extension of the Bliss independence model to pairs: For M drugs, it equals the product of all pair effects to the power 1/(M−1). The pairs model also shows good agreement with previously published data on antibiotic triplets and quadruplets. The present model can only predict combinations at the same doses in which the pairs were measured and is not able to predict effects at other doses. This study indicates that pair-based approaches might be able to usefully predict and prioritize high-order combinations, even in large screens or when material for testing is limited.

Drug cocktails are a promising strategy for diseases such as cancer and infections, because cocktails can be more effective than individual drugs and can overcome problems of drug resistance. However, finding the best cocktail comprising a given set of drugs is challenging because the number of experiments needed is huge and grows exponentially with the number of drugs. This problem is exacerbated when the experiments are expensive and the material for testing is rare. Here, we present a way to address this challenge using a mathematical formula, called the pairs model, that requires relatively few experimental tests in order to estimate the effects of cocktails and to predict which cocktail is most effective. The formula does well on experimental data generated in this study using combinations of between 3 and 6 anticancer drugs, as well as on existing data that use combinations of antibiotics.

Institutional profile: the national Swedish academic drug discovery & development platform at SciLifeLab

The Science for Life Laboratory Drug Discovery and Development Platform (SciLifeLab DDD) was established in Stockholm and Uppsala, Sweden, in 2014. It is one of ten platforms of the Swedish national SciLifeLab which support projects run by Swedish academic researchers with large-scale technologies for molecular biosciences with a focus on health and environment. SciLifeLab was created by the coordinated effort of four universities in Stockholm and Uppsala: Stockholm University, Karolinska Institutet, KTH Royal Institute of Technology and Uppsala University, and has recently expanded to other Swedish university locations. The primary goal of the SciLifeLab DDD is to support selected academic discovery and development research projects with tools and resources to discover novel lead therapeutics, either molecules or human antibodies. Intellectual property developed with the help of SciLifeLab DDD is wholly owned by the academic research group. The bulk of SciLifeLab DDD's research and service activities are funded from the Swedish state, with only consumables paid by the academic research group through individual grants.

New in vitro system to predict chemotherapeutic efficacy of drug combinations in fresh tumor samples

Background

To find the best individual chemotherapy for cancer patients, the efficacy of different chemotherapeutic drugs can be predicted by pretesting tumor samples in vitro via the chemotherapy-resistance (CTR)-Test^®. Although drug combinations are widely used among cancer therapy, so far only single drugs are tested by this and other tests. However, several first line chemotherapies are combining two or more chemotherapeutics, leading to the necessity of drug combination testing methods.

Methods

We established a system to measure and predict the efficacy of chemotherapeutic drug combinations with the help of the Loewe additivity concept in combination with the CTR-test. A combination is measured by using half of the monotherapy’s concentration of both drugs simultaneously. With this method, the efficacy of a combination can also be calculated based on single drug measurements.

Results

The established system was tested on a data set of ovarian carcinoma samples using the combination carboplatin and paclitaxel and confirmed by using other tumor species and chemotherapeutics. Comparing the measured and the calculated values of the combination testings revealed a high correlation. Additionally, in 70% of the cases the measured and the calculated values lead to the same chemotherapeutic resistance category of the tumor.

Conclusion

Our data suggest that the best drug combination consists of the most efficient single drugs and the worst drug combination of the least efficient single drugs. Our results showed that single measurements are sufficient to predict combinations in specific cases but there are exceptions in which it is necessary to measure combinations, which is possible with the presented system.

Predictive Value of Ex Vivo Chemosensitivity Assays for Individualized Cancer Chemotherapy: A Meta-Analysis

Current treatment strategies for chemotherapy of cancer patients were developed to benefit groups of patients with similar clinical characteristics. In practice, response is very heterogeneous between individual patients within these groups. Precision medicine can be viewed as the development toward a more fine-grained treatment stratification than what is currently in use. Cell-based drug sensitivity testing is one of several options for individualized cancer treatment available today, although it has not yet reached widespread clinical use. We present an up-to-date literature meta-analysis on the predictive value of ex vivo chemosensitivity assays for individualized cancer chemotherapy and discuss their current clinical value and possible future developments.

Combination screening in vitro identifies synergistically acting KP372-1 and cytarabine against acute myeloid leukemia

Cytogenetic lesions often alter kinase signaling in acute myeloid leukemia (AML) and the addition of kinase inhibitors to the treatment arsenal is of interest. We have screened a kinase inhibitor library and performed combination testing to find promising drug-combinations for synergistic killing of AML cells. Cytotoxicity of 160 compounds in the library InhibitorSelect™ 384-Well Protein Kinase Inhibitor I was measured using the fluorometric microculture cytotoxicity assay (FMCA) in three AML cell lines. The 15 most potent substances were evaluated for dose-response. The 6 most cytotoxic compounds underwent combination synergy analysis based on the FMCA readouts after either simultaneous or sequential drug addition in AML cell lines. The 4 combinations showing the highest level of synergy were evaluated in 5 primary AML samples. Synergistic calculations were performed using the combination interaction analysis package COMBIA, written in R, using the Bliss independence model. Based on obtained results, an iterative combination search was performed using the therapeutic algorithmic combinatorial screen (TACS) algorithm. Of 160 substances, cell survival was ⩽50% at <0.5μM for Cdk/Crk inhibitor, KP372-1, synthetic fascaplysin, herbimycin A, PDGF receptor tyrosine kinase inhibitor IV and reference-drug cytarabine. KP372-1, synthetic fascaplysin or herbimycin A obtained synergy when combined with cytarabine in AML cell lines MV4-11 and HL-60. KP372-1 added 24h before cytarabine gave similar results in patient cells. The iterative search gave further improved synergy between cytarabine and KP372-1. In conclusion, our in vitro studies suggest that combining KP372-1 and cytarabine is a potent and synergistic drug combination in AML.

Prediction of multidimensional drug dose responses based on measurements of drug pairs

Finding potent multidrug combinations against cancer and infections is a pressing therapeutic challenge; however, screening all combinations is difficult because the number of experiments grows exponentially with the number of drugs and doses. To address this, we present a mathematical model that predicts the effects of three or more antibiotics or anticancer drugs at all doses based only on measurements of drug pairs at a few doses, without need for mechanistic information. The model provides accurate predictions on available data for antibiotic combinations, and on experiments presented here on the response matrix of three cancer drugs at eight doses per drug. This approach offers a way to search for effective multidrug combinations using a small number of experiments.