✍️✍️✍️ Leptins Influences Stress: A Case Study

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Leptins Influences Stress: A Case Study

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Leptin \u0026 Insulin Resistance Balancing Tips w/ Jason Fung, MD

Author : National Research Council U. Subcommittee on protein metabolism in animal feeding. Protein metabolism and nutrition in farm animals: an overview; Mechanisms and regulation of transcription and translation; Mechanisms and regulation in protein degradation; Integration of protein metabolism within the whole body and between Skip to content. Protein Metabolism in Farm Animals.

Energy and protein metabolism and nutrition. Energy and Protein Metabolism and Nutrition. Energy and protein metabolism and nutrition in sustainable animal production. Author : James W. Protein Deposition in Animals. Author : P. Farm Animal Metabolism and Nutrition. Author : J. Energy Metabolism of Farm Animals. Author : K. Collaborator: Corcept Therapeutics Experimental: mifepristone mifepristone mg capsule per day, orally in day cycles. Mifepristone has been demonstrated to improve longevity and quality of life in mice with spontaneous murine cancer without progesterone receptors and in human colon cancer. The present study evaluated the palliative effect of mifepristone in a variety of different types of human cancer. Patients and Methods: Mifepristone was given at mg daily orally with permission from the Food and Drug Administration to people with widely metastatic human cancer no longer responsive to other chemotherapy regimens.

Conclusion: Our data demonstrate a palliative role for the use of mifepristone in cancer therapy. Progesterone receptor antagonists should be given a therapeutic trial in larger controlled studies of various malignancies in humans. All patients reported significant decrease in pain and improved energy within 2 weeks of starting the medication. No one reported any side-effects.

In the United States mifepristone is a restricted drug. It can only be used by licensed abortionists. One needs special permission from the Food and Drug Administration to use it off-label. Approval can only be obtained for cases where no known approved therapy exists or the patient has proven resistant at this time to standard therapy. Antitumor effect of paclitaxel is mediated by inhibition of myeloid-derived suppressor cells and chronic inflammation in the spontaneous melanoma model. J Immunol. Using here the ret transgenic murine melanoma model, which mimics human cutaneous melanoma, we tested effects of ultra-low non-cytotoxic dose paclitaxel on functions of myeloid-derived suppressor cells MDSCs , chronic inflammatory mediators, and T cell activities in the tumor microenvironment in vivo.

Administration of paclitaxel significantly decreased accumulation and immunosuppressive activities of tumor-infiltrating MDSCs without alterations of the bone marrow hematopoiesis. The production of mediators of chronic inflammation in the tumor milieu was also diminished. Importantly, reduced tumor burden and increased animal survival upon paclitaxel application was mediated by the restoration of CD8 T cell effector Killer T Cell functions.

We suggest that the ability of paclitaxel in non-cytotoxic dose to block the immunosuppressive potential of MDSCs in vivo represents a new therapeutic strategy to down-regulate immunosuppression and chronic inflammation in the tumor microenvironment for enhancing the efficacy of concomitant anti-cancer therapies. Loss of donor-mediated immune antitumor activity after allogeneic hematopoietic stem-cell transplantation HSCT permits relapse of hematologic cancers. We hypothesized that immune checkpoint blockade established by targeting cytotoxic T-lymphocyte—associated protein 4 with ipilimumab could restore antitumor reactivity through a graft-versus-tumor effect.

Complete responses occurred in 4 patients with extramedullary acute myeloid leukemia and 1 patient with the myelodysplastic syndrome developing into acute myeloid leukemia. Four patients had a durable response for more than 1 year. In summary, CTLA-4 blockade was a feasible approach for the treatment of patients with relapsed hematologic cancer after transplantation. Complete remissions with some durability were observed, even in patients with refractory myeloid cancers. The growth of non-Hodgkin lymphomas can be influenced by tumor-immune system interactions.

Cytotoxic T-lymphocyte antigen 4 CTLA-4 is a negative regulator of T-cell activation that serves to dampen antitumor immune responses. Blocking anti-CTLA-4 monoclonal antibodies improves host resistance to immunogenic tumors, and the anti-CTLA-4 antibody ipilimumab MDX has clinical activity against melanoma, prostate, and ovarian cancers. Eighteen patients were treated, 12 at the lower dose level and 6 at the higher dose level. Ipilimumab was generally well tolerated, with common adverse events attributed to it, including diarrhea, headache, abdominal pain, anorexia, fatigue, neutropenia, and thrombocytopenia.

Blockade of CTLA-4 signaling with the use of ipilimumab is well tolerated at the doses used and has antitumor activity in patients with B-cell lymphoma. Further evaluation of ipilimumab alone or in combination with other agents in B-cell lymphoma patients is therefore warranted. J Clin Oncol. Epub Jun 6. Cancer cells can exploit the programmed death-1 PD-1 immune checkpoint pathway to avoid immune surveillance by modulating T-lymphocyte activity.

PD-1 blockade has produced significant antitumor activity in solid tumors, and similar evidence has emerged in hematologic malignancies. Patients had received a median of three range, one to 12 prior systemic treatments. Median time of follow-up observation was Durations of response in individual patients ranged from 6. Nivolumab was well tolerated and exhibited antitumor activity in extensively pretreated patients with relapsed or refractory B- and T-cell lymphomas. Additional studies of nivolumab in these diseases are ongoing. For Hodgkin lymphoma HL patients who fail front-line therapy, salvage regimens including autologous and allogeneic hematopoietic stem cell transplant alloHSCT may cure select patients, although relapse remains a significant cause of transplant failure.

Treatment strategies aimed at bolstering post-transplant immunity to both prevent and treat relapse following alloHSCT are of significant interest, especially immune checkpoint inhibitors such as cytotoxic T-lymphocyte-associated protein 4 CTLA-4 and programmed death 1 PD-1 monoclonal Abs. To evade elimination by the host immune system, tumor cells commonly exploit physiological immune checkpoint pathways, restraining efficient anti-tumor immune cell function. Growing understanding of the complex dialog between tumor cells and their microenvironment contributed to the development of immune checkpoint inhibitors.

This innovative strategy has demonstrated paradigm-shifting clinical activity in various malignancies. Antibodies targeting programmed death 1 and cytotoxic T-lymphocyte-associated protein-4 are also being investigated in lymphoid malignancies with varying levels of activity and a favorable toxicity profile. To date, evaluated only in the setting of relapsed or refractory disease, anti-programmed death 1 antibodies such as nivolumab and pembrolizumab show encouraging response rates particularly in classical Hodgkin lymphoma but also in follicular lymphoma and diffuse-large B-cell lymphoma. As the first immune checkpoint inhibitor in lymphoma, nivolumab was approved for the treatment of relapsed or refractory classical Hodgkin lymphoma by the Food and Drug Administration in May In this review, we assess the role of the pathways involved and potential rationale of checkpoint inhibition in various lymphoid malignancies.

In addition to data from current clinical trials, immune-related side effects, potential limitations and future perspectives including promising combinatory approaches with immune checkpoint inhibition are discussed. The single-chain-based bispecific antibodies usually bridge tumor cells with immune cells and form an immunological synapse because of their relatively small size. CD19 is expressed by malignant B cells and all developmental stages of B cells, except plasma cells and pluripotent stem cells of the bone marrow. Blinatumomab is administered as a continuous infusion via an ambulatory pump for 4 to 8 weeks.

Food and Drug Administration FDA today granted accelerated approval to blinatumomab Blincyto for the treatment of patients with Philadelphia chromosome—negative, relapsed or refractory precursor B-cell acute lymphoblastic leukemia B-cell ALL. Published online Apr 4. Benjamin and Anthony S. Barnes, Principal Investigator. Epub Mar 4. Blinatumomab for the treatment of B-cell lymphoma.

Oak E1, Bartlett NL. Among the various formats of bispecific antibodies developed in the past 50 years, the BiTE class is remarkable for its low effector-to-target ratio, high tissue penetration and singular ability to activate T cells independent of MHC class I presentation or costimulation. The authors discuss the potential future directions in light of other new competing therapies for NHL and unmet clinical needs in the market.

Although blinatumomab seems an unlikely option for treating indolent lymphoma due to toxicity, the need for long-term continuous infusion therapy and multiple promising well-tolerated oral agents, it holds promise for aggressive NHL patients whose diseases are refractory to current standard approaches. Recent proliferation of novel targeted therapies in lymphoma has been substantial. B-cell receptor pathway inhibitors and immune checkpoint inhibitors have been a major focus, however significant advances in monoclonal antibodies MoAbs which directly target malignant cells have also occurred. These MoAbs continue to make significant impact in lymphoma management.

Novel dosing schedules of anti-CD20 MoAb rituximab potentially optimise efficacy in specific lymphoma subgroups, as certain populations may be receiving suboptimal doses using current schedules. Next-generation anti-CD20 MoAbs may surpass rituximab in terms of efficacy. MoAbs targeting other B-cell surface antigens and antibody-drug conjugates ADCs have yielded promising data. Bispecific antibodies that can recruit T-lymphocytes to lymphoma cells have also shown efficacy. To further improve outcomes for patients with lymphoma using MoAbs, scrupulous trial design incorporating translational research, and synergistic drug combinations will be required.

This review discusses the mechanisms of action, current data and future directions involving MoAbs. Tara Arvedson, Ph. Recent clinical data have demonstrated the potency and significance of T-cells in anti-tumor activity. We have optimized a plug-and-play, Fc-containing bispecific antibody platform with high stability, efficient production, and antibody-like pharmacokinetics. This optimized bispecific format resembles a standard monoclonal antibody, with one of the Fab arms replaced by a stability-optimized single-chain Fv scFv scFv-Fab-Fc.

We will present application of the platform to create a pipeline of CD3 bispecifics for T-cell redirection, and dual checkpoint blockade bispecifics for T-cell activation. Prasad S. Adusumilli, M. Adaptive resistance developed by the solid tumors following immune attack poses a hurdle for achieving durable responses by both CAR T-cell therapy and checkpoint blockade. Broderick jasoncology Published Online: Thursday, Feb 18, Safety and activity of the anti-CD79B antibody-drug conjugate polatuzumab vedotin in relapsed or refractory B-cell non-Hodgkin lymphoma and chronic lymphocytic leukaemia : a phase 1 study.

Patients with relapsed or refractory B-cell non-Hodgkin lymphoma NHL have an unfavourable prognosis with few treatment options. Polatuzumab vedotin is an antibody-drug conjugate containing an anti-CD79B monoclonal antibody conjugated to the microtubule-disrupting agent monomethyl auristatin E. We aimed to assess the safety and clinical activity of polatuzumab vedotin in relapsed or refractory B-cell NHL and chronic lymphocytic leukaemia CLL. In this phase 1, multicentre, open-label study, we enrolled patients with documented NHL or CLL expected to express CD79B confirmation of CD79B expression was not required and for whom no suitable therapy of curative intent or higher priority existed from 13 centres.

The primary endpoints of the study were to assess safety and tolerability, determine the maximum tolerated dose, and identify the recommended phase 2 dose of polatuzumab vedotin as a single agent and in combination with rituximab. After determination of the recommended phase 2 dose, we enrolled patients with relapsed or refractory diffuse large B-cell lymphoma and relapsed or refractory indolent NHL into indication-specific cohorts. Patients who received any dose of polatuzumab vedotin were available for safety analyses. This study is registered with ClinicalTrials. Between March 21, , and Nov 30, , we enrolled 95 patients 34 to the NHL dose-escalation cohort, 18 to the CLL dose-escalation cohort, 34 with NHL to the expansion cohort at the recommended phase 2 dose, and nine with NHL to the rituximab combination cohort; no expansion cohort of CLL was started due to lack of activity in the dose-escalation cohort.

At the recommended phase 2 dose, objective responses were noted in 23 of 42 activity-evaluable patients with NHL given single-agent polatuzumab vedotin 14 of 25 with diffuse large B-cell lymphoma, seven of 15 with indolent NHL, and two with mantle-cell lymphoma and seven of nine patients treated with polatuzumab vedotin combined with rituximab. No objective responses were observed in patients with CLL.

Its clinical activity should be further assessed in NHL. Patients received a median of 3 prior lines of therapy. Median time since last regimen was 1. Seventeen patients ended treatment in cycle 1 induction , 7 in cycle 2 consolidation , and 1 in retreatment. Three patients had late CR in follow-up without other treatment. Most neurologic events resolved. The flat-dose cohort was stopped because of grade 3 neurologic events in both patients.

Further studies need to define the optimal approach to achieve the target dose without early dropout. The study was registered at www. Patients will participate in a dose-escalation phase Part 1 and dose expansion Part 2. In Part 2, patients will receive the dose level identified in Part 1. ADCT is an antibody drug conjugate ADC composed of a humanized antibody directed against human cluster of differentiation 19 CD19 , stochastically conjugated via a valine-alanine cleavable, maleimide linker to a pyrrolobenzodiazepine PBD dimer cytotoxin. Tremendous advances have been made in the treatment of non-Hodgkin Lymphoma in the past 20 years. However, a significant portion of the patient population still fail to respond to currently available therapies.

ADCT treatment has been reasonably well-tolerated, with the most common emergent adverse events being fatigue, neutropenia, and thrombocytopenia. ADCT enjoys strong relationships with world class partners, including AstraZeneca and its global biologics research and development arm, MedImmune. Check, MD, Ph. Mifepristone has been demonstrated to cause palliation from murine and human cancer, even in cancers not known to be positive for expression of progesterone receptors. The aim of the present study was to determine if rapidly advancing chronic lymphocytic leukemia responds to mifepristone therapy, and if so, is this effect related to increased expression of the progesterone-induced blocking factor?

CASE REPORT: An year-old woman with chronic lymphocytic leukemia whose condition progressed to the acute rapidly progressing stage agreed to be exclusively treated orally with mg mifepristone daily. Complete remission has persisted so far for 12 months on exclusive mifepristone therapy. Her PIBF levels were normal before mifepristone therapy and did not change after treatment. Check JH1,2, Check D2. Laurel, NJ, U. Progesterone induced blocking factor PIBF is a unique protein that is not present in normal cells, but is found predominantly in rapidly growing cells of the fetal placental unit or cancer cells. The parent compound is cleaved into smaller intracytoplasmic isoforms that can suppress cellular immune response, especially, but not limited to natural killer cells.

The progesterone receptor antagonist mifepristone can suppress messenger RNA for PIBF, but can also suppress the intracytoplasmic protein. Treating cancer cell lines, intact animals with a variety of spontaneous cancers, and people with various cancers with mifepristone, has been found to inhibit cancer growth, and provide both palliation of symptoms and longevity possibly by suppressing this unique immunomodulatory protein.

Epub Sep Progesterone and the immunology of pregnancy. RNAP motion during transcription brings the beads in closer and their relative distance changes, which can then be recorded at a single nucleotide resolution. The sequence is deduced based on the four readouts with lowered concentrations of each of the four nucleotide types, similarly to the Sanger method. Other high speed gene sequencers can be used. The system applies pharmacogenomic information to select the best medication and identify people who need an unusually high or low dose.

This is in addition to clinical factors, such as a patient's age, weight, sex, and liver and kidney function. Pharmacogenomics sometimes called pharmacogenetics is focused on understanding how genes affect individual responses to medications and to help doctors select the drugs and dosages best suited for each person. Pharmacogenomics looks at variations in genes for proteins that influence drug responses. Such proteins include a number of liver enzymes that convert medications into their active or inactive forms. Even small differences in the genetic sequences of these enzymes can have a big impact on a drug's safety or effectiveness.

One example involves a liver enzyme known as CYP2D6. This enzyme acts on a quarter of all prescription drugs, including the painkiller codeine, which it converts into the drug's active form, morphine. The CYP2D6 gene exists in more than different versions, many of which vary by only a single difference in their DNA sequence, although some have larger changes. The majority of these variants don't affect drug responses. Some people have hundreds or even thousands of copies of the CYP2D6 gene typically, people have two copies of each gene. Those with extra copies of this gene manufacture an overabundance of CYP2D6 enzyme molecules and metabolize the drug very rapidly.

As a result, codeine may be converted to morphine so quickly and completely that a standard dose of the drug can be an overdose. On the other end of the spectrum, some variants of CYP2D6 result in a nonfunctional enzyme. People with these variants metabolize codeine slowly, if at all, so they might not experience much pain relief. For these people, doctors might prescribe a different type of pain reliever. Pharmacogenomic information can cover dosage guidance, possible side effects or differences in effectiveness for people with certain genomic variations—can help doctors tailor their drug prescriptions for individual patients. The system applies pharmacogenomic data to develop and market drugs for people with specific genetic profiles.

The system can identify the genetic basis for certain serious side effects, drugs could be prescribed only to people who are not at risk for them. As a result, potentially lifesaving medications, which otherwise might be taken off the market because they pose a risk for some people, could still be available to those who could benefit from them. For example, a few drug and gene associations are listed in the Appendix. It will be recognized by the skilled artisan that while the computer visualization for drug interaction information retrieval logic is shown to execute in a single host computing platform , the invention is not so limited and the computer visualization for drug interaction information retrieval logic also can be distributed in form across multiple different computing platforms.

Further, the camera and marshalling apparatus can be located remotely from the host computing platform whilst providing acquired imagery to the host computing platform over a computer communications network, whether wireless or wirebound. Yet further, either or both of the drug image data store and the drug interaction data store can be remotely disposed from the host computing platform and accessible over a computer communications network, whether wireless or wirebound. Having the ability to look at a patient on an individual basis will allow for a more accurate diagnosis and specific treatment plan. Genotyping is the process of obtaining an individual's DNA sequence by using biological assays. By having a detailed account of an individual's DNA sequence, their genome can then be compared to a reference genome, like that of the Human Genome Project, to assess the existing genetic variations that can account for possible diseases.

An individual's genetic make-up also plays a large role in how well they respond to a certain treatment, and therefore, knowing their genetic content can change the type of treatment they receive. The system applies pharmacogenomics by using an individual's genome to provide a more informed and tailored drug prescription. Often, drugs are prescribed with the idea that it will work relatively the same for everyone, but in the application of drugs, there are a number of factors that must be considered.

The detailed account of genetic information from the individual will help prevent adverse events, allow for appropriate dosages, and create maximum efficacy with drug prescriptions. The pharmacogenomic process for discovery of genetic variants that predict adverse events to a specific drug has been termed toxgnostics. In addition to specific treatment, personalized medicine can greatly aid the advancements of preventive care. For instance, many women are already being genotyped for certain mutations in the BRCA1 and BRCA2 gene if they are predisposed because of a family history of breast cancer or ovarian cancer.

As more causes of diseases are mapped out according to mutations that exist within a genome, the easier they can be identified in an individual. Measures can then be taken to prevent a disease from developing. Even if mutations were found within a genome, having the details of their DNA can reduce the impact or delay the onset of certain diseases. Having the genetic content of an individual will allow better guided decisions in determining the source of the disease and thus treating it or preventing its progression. This will be extremely useful for diseases like Alzheimer's or cancers that are thought to be linked to certain mutations in human DNA. This technology is an assay that is developed during or after a drug is made available on the market and is helpful in enhancing the therapeutic treatment available based on the individual.

These companion diagnostics have incorporated the pharmacogenomic information related to the drug into their prescription label in an effort to assist in making the most optimal treatment decision possible for the patient. Having an individual's genomic information can be significant in the process of developing drugs as they await approval from the FDA for public use. Having a detailed account of an individual's genetic make-up can be a major asset in deciding if a patient can be chosen for inclusion or exclusion in the final stages of a clinical trial. Being able to identify patients who will benefit most from a clinical trial will increase the safety of patients from adverse outcomes caused by the product in testing, and will allow smaller and faster trials that lead to lower overall costs.

In addition, drugs that are deemed ineffective for the larger population can gain approval by the FDA by using personal genomes to qualify the effectiveness and need for that specific drug or therapy even though it may only be needed by a small percentage of the population. Treatments can be more specifically tailored to an individual and give insight into how their body will respond to the drug and if that drug will work based on their genome. The personal genotype can allow physicians to have more detailed information that will guide them in their decision in treatment prescriptions, which will be more cost-effective and accurate.

The system next generates gene-environmental factor interactions to help lifestyle recommendations. The system creates a matrix that correlates gene and environmental impacts. One embodiment generates gene based drug-drug interactions that allow the physician or pharmacist to avoid health problems for the patient. The process includes the following: At , construct a comprehensive gene-drug-drug interactions GDDIs training dataset that includes all pharmaceutical, pharmacokinetic PK , pharmacogenetic PG ,and pharmacodynamic PD GDDIs from multiple data sources for each drug in a set of drugs under consideration. In an embodiment, the multiple data sources can include, but are not limited to, the following: gene sequencers, clinical trials; drug development information; empirical information; a drug bank; drug label information; an adverse event reporting system e.

At step , construct side effect features for each of the drugs in the set from genetic panels for an individual and side effects associated with the drugs in the set. At , build, using the GDDIs training dataset, a GDDIs classifier for predicting whether or not a given drug pair derived from the set of drugs results in adverse interactions, and repeat this process for all possible drug pairs derivable from the set of drugs. In an embodiment, the features used for building the classifier can include, but are not limited to, the following: drug's clinical side effect keywords; and other drug properties e. At , obtain predicted GDDIs from the classifier. At , for each side effect, perform statistical test to determine whether that side effect is differentially shown between positive predicted GDDIs and negative predicted GDDIs.

Side effects are effects after taking a medicine, which are other than the intended therapeutic effects. In , relative interactions between the different drug substances can be determined by locating references in the interaction data for each of the drug substances to others of the substances. Finally, in block , the relative interactions can be rendered within a report such as a paper report or a graphical user interface display. Optionally, an activatable link can be provided in the display for selected ones of the drug substances for reordering the selected ones of the drug substances. In this way, the relative drug interactions resulting from the dispensing of multiple different drug substances based on patient genetic data can be determined without requiring a tedious manual process of looking up drug interaction data for each substance and manually correlating the drug interaction data for the specific combination of dispensed substances.

The system can also perform GDDI discovery and prediction that uses molecular structure similarity information derived from fingerprint-based modeling. Identifying new GDDIs using structural similarity is based on the basic idea that if drug A interacts with drug B, and drug C is structurally similar to A, then C should also interact with B the argument also follows if A is replaced with B. Hence, by combining knowledge of known interactions with structural similarity it is possible to identify new interactions. The process uses a list of drug—drug interactions from DrugBank step 1 , structural similarity computation was carried out using molecular fingerprints step 2 , apply gene-drug interaction to similar drugs, and a new list of gene-drug interactions can be inferred.

Structural similarity can be identified in three steps: 1 Collecting and processing drug structures: Information on the structures of the compounds in DrugBank is retrieved along with the SMILE code a chemical notation representing a chemical structure in linear textual form. In one embodiment, the molecular fingerprints were compared using Tanimoto coefficient TC. Next, for each drug affected by a particular gene, the process predicts new gene based DDIs.

One embodiment predicts new DDIs reduces to matrix multiplication of the matrices M1, which consists of the established interactions, and M2, which consists of the similarity matrix. The pharmacogenomic information can be applied to drug labeling. One embodiment may contain information on genomic biomarkers and can describe: Drug exposure and clinical response variability Risk for adverse events Genotype-specific dosing Mechanisms of drug action Polymorphic drug target and disposition genes. The information may include specific actions to be taken based on the biomarker information. Pharmacogenomic information can appear in different sections of the labeling depending on the actions. Biomarkers in the table include but are not limited to germ-line or somatic gene variants, functional deficiencies, expression changes, and chromosomal abnormalities; selected protein biomarkers that are used to select patients for treatment are also included.

In one embodiment, the process includes constructing a gene-drug interactions training dataset that includes pharmaceutical, pharmacokinetic or pharmacodynamics, and pharmacogenomics drug-drug interactions for each drug; constructing side effect features for each of the plurality of drugs from side effects associated with the plurality of drugs; running a gene-drug-drug interactions classifier that predicts adverse drug-drug interactions for drug pairs and the genetic scan; and for each of the side effects, performing a Fisher's exact test to determine predicted gene-drug-drug interactions.

Fisher's exact testis a statistical significance test used in the analysis of contingency tables. It is one of a class of exact tests, so called because the significance of the deviation from a null hypothesis e. One embodiment uses an AlexNet: 8-layer architecture, while another embodiment uses a VGGNet: layer architecture each pooling layer and last 2 FC layers are applied as feature vector. For drugs, the indications of use and other drugs used capture most of many important covariates. The system of FIG. Drugs reported frequently with these indications were Disease indications are a significant source of synthetic associations.

The more disproportionately a drug is reported with an indication x axis , the more likely that drug will be synthetically associated. For example, adverse events strongly associated with drugs are retrieved from the drug's package insert. These drug-event pairs represent a set of known strong positive associations. Adverse events related to sex and race are also analyzed. For example, for physiological reasons, certain events predominantly occur in males for example, penile swelling and azoospermia. Drugs that are disproportionately reported as causing adverse events in males were more likely to be synthetically associated with these events. Similarly, adverse events that predominantly occur in either relatively young or relatively old patients are analyzed.

For example, the SIDER database, extracted from drug package inserts, lists 48, drug-event associations for drugs and adverse events that are also covered by the data mining. Offsides recovers Thus, Offsides finds different associations from those reported during clinical trials before drug approval. Polypharmacy side effects for pairs of drugs Twosides are also analyzed. These associations are limited to only those that cannot be clearly attributed to either drug alone that is, those associations covered in Offsides. The database contains an significant associations for which the drug pair has a higher side-effect association score, determined using the proportional reporting ratio PRR , than those of the individual drugs alone.

The system can predict shared protein targets using drug-effect similarities. The side-effect similarity score between two drugs is linearly related to the number of targets that those drugs share. The system can determine relationships between the proportion of shared indications between a pair of drugs and the similarity of their side-effect profiles in Offsides. The system can use side-effect profiles to suggest new uses for old drugs.

While the preferred system predicts existing therapeutic indications of known drugs, the system can recommend drug repurposing using drug-effect similarities in Offsides. Corroboration of class-wide interaction effects with EMRs. The system can identify DDIs shared by an entire drug class. The class-class interaction analysis generates putative drug class interactions. The system analyzes laboratory reports commonly recorded in EMRs that may be used as markers of these class-specific DDIs.

The system can be used systematic drug surveillance. The FDA manages a collection of adverse drug event reports to monitor the safety of drugs. They rely on physicians, pharmaceutical companies, and patients to volunteer these reports. Since reporting is not mandatory, many adverse drug events that occur are never reported to the FDA. To address this issue, an embodiment of the present invention uses an algorithm to infer unreported adverse drug events. This embodiment relies on the fact that many adverse events occur together. For example, nausea and vomiting commonly manifest together. Therefore, if a drug is observed to causes nausea, it can be inferred that it also causes vomiting.

The successful prediction of side effects before a drug enters clinical trials can be done. Chemical informatics techniques can predict drug side effects by comparing the structural similarity of drugs. Protein structural similarity is learned by the deep learning system to predict drug side effects. More recently, network and chemical properties are used for predictive models of drug effects and leverage the system's comprehensive database of known drug effects. In a parallel trend, anti-biotics and cancer treatments have lost their effect over time.

Thus, the processor can analyze evolutions in the target of the treatment and recommend alternative treatment. In one aspect, systems and methods includes analyzing a disease state of a subject by collecting genetic profile data on a population of tumors and original tumor treatment s ; identifying one or more evolutionary paths of escape and evolved tumor treatment s ; and based on a subject profile, predicting a probability of escape along the one or more evolutionary paths.

In another aspect, a method for analyzing a disease state of a subject includes capturing a first liquid biopsy from the subject; providing the liquid biopsy to a genetic analyzer to identify the subject's genetic information of a first disease state at a first time point; searching for genetically similar patients and predicting a mutation of the disease into a second disease state at a second time point; analyzing a treatment database and recommending a treatment given the first and second disease states; capturing a second liquid biopsy from the subject at a second time point; providing the second liquid biopsy to tje genetic analyzer to identify the subject's genetic information; and if the genetic information from the second time point matches the predicted mutation, continuing the recommended treatment for the subject and otherwise changing the recommended treatment.

In a further aspect, a method for analyzing a disease state of a subject includes capturing a first liquid biopsy from the subject; providing the liquid biopsy to a genetic analyzer to identify the subject's genetic information of a first disease state at a first time point; searching for genetically similar subject profiles and predicting a mutation of the disease into a second disease state at a second time point; capturing a second liquid biopsy from the subject; providing the second liquid biopsy to a genetic analyzer to identify the subject's genetic information at a second time point; and if the genetic information from the second time point matches the predicted mutation, continuing the recommended treatment for the subject and otherwise changing the recommended treatment.

In another aspect disclosed herein is a method for analyzing a disease state of a subject by characterizing the subject's genetic information at two or more time points with a genetic analyzer, e. In another aspect, a method detects a trend in the amount of mutation cancer polynucleotides in a sample from a subject over time by determining a frequency of the cancer polynucleotides at a plurality of time points; determining an error range for the frequency at each of the plurality of time points; determining, between an earlier and later time point, whether error ranges 1 overlap, indicating stability of frequency, 2 an increase at the later time point outside the error range, indicating increase in frequency or 3 a decrease at the later time point outside the error range, indicating decrease in frequency.

In yet another aspect, a method detects mutation cellular activities by sequencing of cell-free nucleic acid with a genetic analyzer, e. A genetic analyzer includes any system for genetic analysis, e. In another aspect, a method detects a mutation in a cell-free or substantially cell free sample obtained from a subject by generating consensus sequences by comparing later e. In another aspect disclosed herein is a method to detect mutation cellular activities by providing at least one set of tagged parent polynucleotides, and for each set of tagged parent polynucleotides; amplifying the tagged parent polynucleotides in the set to produce a corresponding set of amplified progeny polynucleotides; with a genetic analyzer, e. In yet another aspect, a method detects a mutation in a cell-free or substantially cell free sample obtained from a subject by sequencing extracellular polynucleotides from a bodily sample from a subject with a genetic analyzer, e.

The method identifies one or more evolutionary paths of escape and evolved tumor treatment s. These paths are caused by various drivers. For example, as shown in FIG. Double lines are used when differentiating heterozygous and homozygous changes is useful. Dots represent single nucleotide changes, whereas lines and arrows represent structural changes. Mutations and genetic alterations including in copy number, for example, allelic imbalances, chromosomal copy number changes, such as amplifications, deletions, aneuploidy, loss of heterozygosity, and micro-satellite instability are often found to be associated with a disease state, for example, cancer.

It has been observed that alterations in chromosomal copy number and loss of heterozygosity LOH are forms of genetic changes that often signal the activation of oncogenes and inactivation of tumor suppressor genes anti-oncogenes. Variations in the form of copy number polymorphisms CNP can also occur in normal individuals. Identification of the loci implicated in these aberrations can generate anchor points which facilitate oncogenomics and toxicogenomics studies.

Subsequently the shared LOH and aberrant CN regions can be used to partition the transcriptome data and track the differential transcript expression in the affected genomic segments. Locating and exploring such alteration events is an important research approach toward understanding the cause and progression of disease. For diploid organisms, the abnormal chromosomal state results when the normal diploid distribution is perturbed, resulting in changes that can include, for example, deletions, amplifications and translocations.

Deletions can be of a partial chromosome ranging from micro-deletions on the order of several kb to macro-deletions of mega bases, entire arms of a chromosome or entire chromosomes. Amplifications can range from partial chromosomal amplifications to gains of a single copy of a chromosome to multiple copy gains of one or more chromosomes. Translocations generally comprise parts of a first chromosome being translocated to another chromosome. Each oval shape X 1 , X 2 , X 3 represents a random variable that can adopt any of a number of values.

The arrows in the diagram often called a trellis diagram denote conditional dependencies. This is called the Markov property. Similarly, the value of the observed variable y t representing the mutation conditions only depends on the value of the hidden variable x t both at timet. The parameters of a hidden Markov model are of two types, transition probabilities and emission probabilities also known as output probabilities. The transition probabilities control the way the hidden state at time is chosen given the hidden state at time. The hidden state space is assumed to consist of one of possible values, modeled as a categorical distribution. See the section below on extensions for other possibilities. This means that for each of the possible states that a hidden variable at time can be in, there is a transition probability from this state to each of the possible states of the hidden variable at time, for a total of transition probabilities.

Note that the set of transition probabilities for transitions from any given state must sum to 1. Thus, the matrix of transition probabilities is a Markov matrix. Because any one transition probability can be determined once the others are known, there are a total of transition parameters. In addition, for each of the possible states, there is a set of emission probabilities governing the distribution of the observed variable at a particular time given the state of the hidden variable at that time.

The size of this set depends on the nature of the observed variable. For example, if the observed variable is discrete with possible values, governed by a categorical distribution, there will be separate parameters, for a total of emission parameters over all hidden states. On the other hand, if the observed variable is an -dimensional vector distributed according to an arbitrary multivariate Gaussian distribution, there will be parameters controlling the means and parameters controlling the covariance matrix, for a total of emission parameters.

In such a case, unless the value of is small, it may be more practical to restrict the nature of the covariances between individual elements of the observation vector, e. The HMM method can model a somatic evolution of cancer. The method includes modeling genetic instability, which results in abnormal numbers of chromosomes or aneuploidy, elevated mutation rates, and altered distributions of mutational patterns. The method can identify one or more cancer mutation drivers. These drivers include those that disrupt cellular signaling pathways essential for multicellular organisms and possible mutations that increase somatic fitness of cancer cells. The method can include identifying dynamics of tumor progression in a population based on interactions with an environment.

The method includes collecting repeated genetic observations to enhance statistical inference about the evolution of tumors. The method includes recommending or providing a therapeutic regimen in anticipation of the one or more escape paths. Diagnosis of cancer can be done by analyzing the genetic variants, even in the presence of noise. The analysis can be based on the frequency of Sequence Variants or Level of CNV and a diagnosis confidence indication or level for detecting genetic variants in the noise range can be established.

The process increases the diagnosis confidence using a plurality of measurements to increase confidence of Diagnosis 6 , or alternatively using measurements at a plurality of time points to determine whether cancer is advancing, in remission or stabilized. The diagnostic confidence can be used to identify disease states. For example, cell free polynucleotides taken from a subject can include polynucleotides derived from normal cells, as well as polynucleotides derived from diseased cells, such as cancer cells. Polynucleotides from cancer cells may bear genetic variants, such as somatic cell mutations and copy number variants. When cell free polynucleotides from a sample from a subject are sequenced, these cancer polynucleotides are detected as sequence variants or as copy number variants.

Tested over time, one can determine whether a cancer is advancing, stabilized or in remission by comparing confidence intervals over time. Where the confidence intervals do not overlap, this indicates the direction of disease. In one implementation, using measurements from a plurality of samples collected substantially at once or over a plurality of time points, the diagnostic confidence indication for each variant can be adjusted to indicate a confidence of predicting the observation of the CNV or mutation.

The confidence can be increased by using measurements at a plurality of time points to determine whether cancer is advancing, in remission or stabilized. The diagnostic confidence indication can be assigned by any of a number of known statistical methods is assigned and can be based, at least in part, on the frequency at which the measurements are observed over a period of time. For example, a statistical correlation of current and prior results can be done. Alternatively, for each diagnosis, a hidden Markov model can be built, such that a maximum likelihood or maximum a posteriori decision can be made based on the frequency of occurrence of a particular test event from a plurality of measurements or a time points.

As part of this model, the probability of error and resultant diagnostic confidence indication for a particular decision can be output as well. In this manner, the measurements of a parameter, whether or not they are in the noise range, may be provided with a confidence interval. Tested over time, one can increase the predictive confidence of whether a cancer is advancing, stabilized or in remission by comparing confidence intervals over time.

Two time points can be separated by about a month to about a year, about a year to about 5 years, or no more than about three months. The HMM detect with high sensitivity genetic variation in a sample of initial genetic material. The methods involve using one to three of the following tools: First, the efficient conversion of individual polynucleotides in a sample of initial genetic material into sequence-ready tagged parent polynucleotides, so as to increase the probability that individual polynucleotides in a sample of initial genetic material will be represented in a sequence-ready sample. This can produce sequence information about more polynucleotides in the initial sample. Second, high yield generation of consensus sequences for tagged parent polynucleotides by high rate sampling of progeny polynucleotides amplified from the tagged parent polynucleotides, and collapsing of generated sequence reads into consensus sequences representing sequences of parent tagged polynucleotides.

Third, the noise in the detection of mutations and copy number variations is reduced by comparing prior sample analysis with the current sample and increasing a diagnostic confidence indication if the same mutations and copy number variations have appeared in prior analysis and otherwise decreasing the diagnostic confidence indication if this is the first time the sequence is observed.

The subclones are related to each other by an evolutionary process of acquisition of mutations. In this example, the three clones leaf nodes are characterized by different combinations of the four single nucleotide variant SNV sets A, B, C, and D. The percentages on the edges of the tree indicate the fraction of cells with this particular set of SNVs, e. The evolutionary history of a tumor gives rise to a heterogeneous collection of normal cells small discs and cancer subclones large discs, triangles, squares. Internal nodes that have been fully replaced by their descendants like the one carrying SNV sets A and B without C or D are no longer part of the tumor.

Embodiments of the invention can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both hardware and software elements. In a preferred embodiment, the invention is implemented in software, which includes but is not limited to firmware, resident software, microcode, and the like. Furthermore, the invention can take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can contain, store, communicate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system or apparatus or device. Examples of a computer-readable medium include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random access memory RAM , a read-only memory ROM , a rigid magnetic disk and an optical disk. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution. Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks.

Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters. For example, K vectors are randomly chosen and assigned as a cluster center for applying k-means clustering algorithms. In pattern recognition, the k-means is a method for classifying objects based on the closest training examples in the feature space. The Euclidian distance between different patterns in this vector space may be used to find clusters of patterns. The system may assign a new input vector to its closest cluster center and may move that cluster towards the input vector by a fraction of the Euclidean distance between them. The system may use knowledge-based components such as a knowledge-based repository KB. The repository may include clinical information.

The knowledge-based repository may act as a repository for some or all of the referenced knowledge. For example, it can include reference values for certain consents and variables used for inference. Accordingly, one or more layers e. For example, one or more of the services may query the knowledge-based repository when making an inference. In another embodiment, an ontology-based approach may be used. For example, the knowledge-based repository may leverage the semantic web using techniques, such as statistical relational learning SRL. SRL may expand probabilistic reasoning to complex relational domains, such as the semantic web. The SRL may achieve this using a combination of representational formalisms e. This relationship may be given a weight e.

By leveraging the knowledge in the semantic web e. Such sentiments and the volume of discussions may then be transformed into weights. Accordingly, although the system originally assigned a weight of 0. In an embodiment, Markov logic may be applied to the semantic web using two objects: first-order formulae and their weights. The formulae may be acquired based on the semantics of the semantic web languages.

In one embodiment, the SRL may acquire the weights based on probability values specified in ontologies. In another embodiment, where the ontologies contain individuals, the individuals can be used to learn weights by generative learning. These techniques may be used to not only to obtain first-order waited formulae for clinical parameters, but also general information. This information may then be used when making inferences. If data on patients with obesity and as to whether they were diagnosed with diabetes or not is available, then the weights for this relationship may be learnt from the data.

This may be extended to non-clinical examples such as person's mood, beliefs etc. The pattern recognizer may use the temporal dimension of data to learn representations. The pattern recognizer may include a pattern storage system that exploits hierarchy and analytical abilities using a hierarchical network of nodes. The nodes may operate on the input patterns one at a time. For every input pattern, the node may provide one of three operations: 1. Storing patterns, 2. Learning transition probabilities, and 3. Context specific grouping.

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