Too many combinations to test

When two expensive cancer drugs are combined, the price is often the sum of both even though the extra benefit is smaller. A rule for splitting the total value between them is needed.

If a company refuses to supply its approved drug for a well-designed independent trial combining it with a rival's drug, the law would let the trial buy it at manufacturing cost, with results shared back.

Radiotherapy may make immunotherapy work better, but the trials to test this are scattered and often small. One shared platform, run by radiotherapy groups with drugs supplied by several companies, would settle it faster.

Build a shared, not-for-profit clinical unit that runs early combination trials to a standard recipe, so that small companies and academics can test pairs without building their own trial machinery.

Companies fear that testing a combination will hand a competitor a patent. A shared pool where combination patents are cross-licensed by default would remove the fear.

Trials that test many companies' drugs side by side against one shared control work best when nobody's company runs them. A standing non-profit sponsor would make this the norm rather than a rare exception.

Instead of starting a new trial for every drug pair, keep one always-open trial per cancer that new arms can join and leave, sharing the same control group.

Build a large, openly shared dataset of how tumour organoids respond to drug pairs, so that anyone can look up which combinations might work for which tumour type.

A government or charity holds stocks of experimental cancer drugs under standing agreements, so academic doctors can test combinations without negotiating with each company separately.

Trials tell us a drug works but not where it fits among the others. Commit to answering 'which order' from hospital data within a year of each approval.

Record, for every patient, the order of treatments and what happened, so that the most common sequences can be compared and the worst ones flagged.

Most of the delay in testing two companies' drugs together is lawyers negotiating from scratch. A single standard agreement, blessed by regulators, would let them sign in weeks.

Companies with drugs that might work together rarely test them because the legal negotiation takes longer than the trial. A pre-written standard contract would fix that.

Radiotherapy is given to half of all cancer patients but few new drugs are tested alongside it. A permanent trial platform would test drug-plus-radiation pairs systematically.

Rather than building a new trial from scratch for every drug, keep one permanent trial open per cancer where new treatments can be slotted in and dropped out, sharing the same patients, control group and infrastructure.

Train a model on millions of experiments where genes and drugs were altered, so it can predict the effect of a new combination without running the experiment.

If most tumours escape a drug by the same back-up route, blocking that route from the start may prevent resistance rather than chase it.

Companies would put their cancer drugs into a shared licensing pool so that any qualified investigator can test combinations of drugs from different owners under one standard agreement, with royalties split by a fixed formula.

When a tumour evolves resistance to one drug, it sometimes becomes weaker against another. Map these trade-offs systematically so doctors can pick the next drug to exploit them.

Use existing cell-line and organoid data to score thousands of drug pairs, publish the ranking openly, and only test the top of the list in people.

Ask experts and models to predict, in public, which registered combination trials will meet their endpoint. Track who is right, and use the best forecasters to decide what to fund.

Build a shared, openly available AI model that has learned how cancer cells respond to genetic and drug perturbations, so any lab can predict what a new drug or combination might do.

There are far more possible drug combinations than can ever be tried in patients. Test thousands on living samples of real tumours, then feed only the winners into adaptive trials.

Group patients by why their last drug stopped working, then test the combination designed to fix that specific failure, whatever the cancer.

Choose two treatments so that whatever the tumour does to escape the first, it becomes easier to kill with the second. The immune system is a good candidate partner.

Many combinations are already used off-label. Careful analysis of what happened to those patients can rule out the pairs that clearly do not help before spending money on trials.

Require that approved cancer drugs come with a standard set of data (blood levels, drug interactions, toxicity profile) so anyone can design a safe combination trial without asking the company.

When two cancer drugs are combined, each is usually given at its full single-agent dose, which often proves too toxic. Testing a grid of dose pairs would find combinations that work with tolerable side effects.

One large trial can test aspirin, a statin, metformin and exercise at the same time by randomising each separately, answering four questions for the price of one.

Combination trials usually keep one drug at full dose and push the other as high as patients can bear. Testing a grid of dose pairs would find combinations that work at lower, safer doses.

While patients are treated in a platform trial, their tumour cells grow in a dish and are tested against dozens of drug pairs. The pairs that win in the dish become the next arms.

A rice-grain-sized implant can release small doses of many different drugs into separate spots of a tumour, then be removed so doctors can see which one worked in that person.

Simulate trials of drug combinations in populations of virtual patients to decide which real trials to run, and keep score of how often the simulations were right.

A blood test at six weeks can show whether a treatment is doing anything. Trials should use it to drop failing combinations fast and move patients on.

As results come in, the trial sends more new patients to the arms that are working and fewer to those that are not, so more people benefit and bad arms die faster.

Children's cancers are treated with combinations, but companies study new drugs in children one at a time. Approvals should require the combination study children actually need.

Simulate how two drugs interact in the body and the tumour to pick a starting dose and schedule, instead of guessing from single-drug data.

Regulators should refuse to approve a two-drug combination unless there is evidence that both drugs are doing something, so patients are not exposed to useless extra toxicity and cost.

Adding a new arm to an international platform trial currently needs approval in every country again. A single, pre-agreed process would let arms open in weeks.

Insurers already pay for many untested drug combinations. Paying only when the patient joins a simple randomised comparison would turn that spending into evidence.

Give combinations before surgery and look at how much tumour is left when it is removed. That answer comes in months, so many pairs can be tested quickly.

When two approved drugs are both reasonable next steps and nobody knows which should come first, let the clinic flip a coin and record what happens.

Hospitals rotate antibiotics to stop bacteria adapting. Cycling between two cancer drugs on a set schedule, rather than using one until it fails, might work the same way.

Patients are randomised at each decision point, not just at the start, so one trial can compare whole treatment sequences rather than single drugs.

When five companies each run a trial against the same standard treatment in the same patients, let them pool the standard-treatment patients so fewer people are randomised to the old drug.

Two drugs might work better given in turns rather than together, with less toxicity. Almost no trial has tested this.

Targeted drugs briefly make cancer cells easier for the immune system to spot. Giving immunotherapy exactly in that window, rather than at the same time, may work better.

Give patients a short course of one of several drug pairs in the gap before surgery and compare what happened inside the tumours. It is the fastest human test of whether a combination does anything.

Low doses of drugs that change how DNA is packaged can make cancer cells display more of what marks them as abnormal, potentially waking up immunotherapy in cold tumours.

Some everyday medicines, such as antibiotics or steroids, seem to blunt immunotherapy. Automatically scanning health records for such harmful pairs would catch them years earlier.

Build a computer model of each patient's cancer and body that simulates how different treatments would go, and prove in a proper trial that choosing treatment with the model helps.